A salt formed by mixing one or more aliphatic acids with a volatile base may be combined with a pesticidal composition (and/or, in some cases, an adjuvant for a pesticidal composition). Adding the volatile base can improve solubility of the one or more aliphatic acids in at least some pesticidal compositions and/or adjuvants, better promote a target pH of the resulting pesticidal composition, and/or promote bioactivity of the one or more aliphatic acids post-application. The composition can be applied together with a pesticide, e.g. by spraying, to control plant pests.
A01N 57/12 - Biocides, pest repellants or attractants, or plant growth regulators containing organic phosphorus compounds having phosphorus-to-oxygen bonds or phosphorus-to-sulfur bonds containing acyclic or cycloaliphatic radicals
A01N 59/00 - Biocides, pest repellants or attractants, or plant growth regulators containing elements or inorganic compounds
2.
SYSTEMS AND METHODS FOR PREDICTING SOIL PROPERTIES
Machine learning models for generating predictions of soil properties based on Raman spectral measurements are provided. The models can be trained on synthetic training data with associated synthetic Raman spectral measurements and ground truth training data with associated ground truth Raman spectral measurements. Systems and methods for training such machine learning models are also provided. Predictions may be facilitated by generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Soil properties may comprise sequestered soil carbon, nitrogen, phosphorous, potassium, and other constituents. Synthetic and ground-truth modes of training may differ inthe number and type of input provided.
Enhanced pesticidal compositions as well as adjuvants and methods for enhancing pesticidal compositions are provided. Combinations of adjuvants and aliphatic acids which, in combination, can provide improved penetration of aliphatic acids and/or pesticidal active ingredients through waxy plant cuticles (e.g. leaf cuticles) are provided. The adjuvants may comprise non-ionic surfactants, such as ethoxylated fatty alcohols and/or alky aryl polyethoxy late. The aliphatic acids may comprise C4-C12 saturated or unsaturated aliphatic acids, such as trans-2-hexenoic acid, trans-3-hexenoic acid, 5-hexenoic acid, and/or 3-nonenoic acid. The pesticidal active ingredients may comprise strobilurins, and/or azoles, such as pyraclostrobin, azoxystrobin, epoxiconazole, and/or prothioconazole.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
Compositions and methods for increasing the efficacy of pesticidal compositions are described herein, including synergistic pesticidal compositions and methods for delivery of pesticidal active ingredients. Some pesticidal compositions and methods as described are directed to compositions and methods for increasing the efficacy of insecticides, including ryanoid and ryanodine receptor modulator insecticide active ingredients. Methods for enhancing the activity of pesticidal active ingredients in pesticidal compositions in use are also described.
A01N 43/713 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with four or more nitrogen atoms as the only ring hetero atoms
Machine learning models for generating predictions of sequestered soil carbon content are provided. The models can be trained on synthetic training data with associated synthetic spectral measurements and ground truth training data with associated ground truth spectral measurements. Systems and methods for training such machine learning models are also provided. Prediction of sequestered soil carbon may be facilitated by using Raman spectral measurements, generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Synthetic and ground-truth modes of training may differ in the number and type of input provided.
Machine learning models for generating predictions of soil properties based on Raman spectral measurements are provided. The models can be trained on synthetic training data with associated synthetic Raman spectral measurements and ground truth training data with associated ground truth Raman spectral measurements. Systems and methods for training such machine learning models are also provided. Predictions may be facilitated by generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Soil properties May comprise sequestered soil carbon, nitrogen, phosphorous, potassium, and other constituents. Synthetic and ground-truth modes of training may differ in the number and type of input provided.
Pesticidal compositions comprising a pesticidal natural oil (and/or a derivative, a synthetic analog, and/or an agriculturally compatible salt thereof), and an anionic surfactant are disclosed. The anionic surfactant may be, e.g., sodium lauryl sulfate, sodium laureth sulfate, sodium myreth sulfate, ammonium lauryl sulfate, and/or docusate sodium salt. The pesticidal natural oil may be, e.g., oleic acid, butyl lactate, thyme oil, thymol, wintergreen oil, methyl salicylate, and/or geraniol.
A01N 65/22 - Lamiaceae or Labiatae [Mint family], e.g. thyme, rosemary, skullcap, selfheal, lavender, perilla, pennyroyal, peppermint or spearmint
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 31/08 - Oxygen or sulfur directly attached to an aromatic ring system
A01N 37/06 - Unsaturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 37/36 - Biocides, pest repellants or attractants, or plant growth regulators containing organic compounds containing a carbon atom having three bonds to hetero atoms with at the most two bonds to halogen, e.g. carboxylic acids containing at least one carboxylic group or a thio-analogue, or a derivative thereof, and a singly bound oxygen or sulfur atom attached to the same carbon skeleton, this oxygen or sulfur atom not being a member of a carboxylic group or of a thio-analogue, or of a derivative thereof, e.g. hydroxy-carboxylic acids
A01N 37/40 - Biocides, pest repellants or attractants, or plant growth regulators containing organic compounds containing a carbon atom having three bonds to hetero atoms with at the most two bonds to halogen, e.g. carboxylic acids containing at least one carboxylic group or a thio-analogue, or a derivative thereof, and a singly bound oxygen or sulfur atom attached to the same carbon skeleton, this oxygen or sulfur atom not being a member of a carboxylic group or of a thio-analogue, or of a derivative thereof, e.g. hydroxy-carboxylic acids having at least one oxygen or sulfur atom attached to an aromatic ring system having at least one carboxylic group or a thio-analogue, or a derivative thereof, and one oxygen or sulfur atom attached to the same aromatic ring system
Pesticidal compositions comprising a spinosyn active ingredient and a salicy late solvent operable to dissolve the spinosyn active ingredient are provided. The pesticidal composition may comprise an emulsion and/or an emulsifiable concentrate (EC) and may comprise an emulsifier. The pesticidal composition may comprise a carrier oil, such as a neutral oil, e.g. to limit potential phytotoxicity induced by the salicy late solvent. The pesticidal composition may comprise an oil-soluble saturated or unsaturated aliphatic acid dissolvable in the salicylate solvent and/or the carrier oil. The pesticidal composition may comprise one or more diluents, fragrance additives, ultraviolet light blockers, and/or other additives. The pesticidal composition may comprise a solution, emulsion, emulsifiable concentrate, and/or any other suitable composition of spinosyn active ingredient and salicylate solvent.
A01N 43/22 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with one or more oxygen or sulfur atoms as the only ring hetero atom with one hetero atom rings with more than six members
Systems and methods for training a soil mapping model are provided, as well as systems and methods for generating soil maps with such trained soil mapping models. The soil mapping models predict soil characteristics (e.g. carbon content) across an area of interest. The training method can involve refining the parameters of a soil map model by estimating the soil mapping model's uncertainty at various locations across an area of interest, selecting relatively high-uncertainty areas, identifying sampling locations within those areas for further samples to be collected, and further training the parameters of the soil mapping model based on the newly-collected samples.
Systems and methods for training a machine learning model for generating a structural representation of a plant are provided, as well as systems and methods for generating a structural representation of a plant via such a model. The training method involves encoding a plant image into a structural representation of the plant (e.g. a “skeleton”), decoding the structural representation of the plant into a reconstructed image of the plant, and classifying the reconstructed image as having been generated based on a ground-truth structural representation or output of the encoder. Such classification incentivizes the encoder to produce structural representations which do not “smuggle” texture information (e.g. appearance, such as color). Texture information may be separately represented. The encoder, once trained, may be used to generate structural representations from plant images without necessarily requiring decoding or classification.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/86 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using syntactic or structural representations of the image or video pattern, e.g. symbolic string recognitionArrangements for image or video recognition or understanding using pattern recognition or machine learning using graph matching
11.
SYSTEMS AND METHODS FOR HYPERSPECTRAL IMAGING OF PLANTS
Systems and methods for hyperspectral imaging of plants are provided. Multispectral images of plants are transformed, e.g. by interpolation along a spectral axis, to generate hyperspectral images of plants. The transformation can be based on spectral bases formed from hyperspectral sample images including images of plant matter. Plant characteristics, such as plant health, may be predicted based on the hyperspectral image. Plant health may be predicted by comparing derivatives of reflectance values with respect to wavelength for a plant of a given image relative to a reference derivative based on a reference hyperspectral image. The derivatives may be compared by determining a regression loss.
G01N 21/3563 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solidsPreparation of samples therefor
Systems and methods for hyperspectral imaging of plants are provided. Multispectral images of plants are transformed, e.g. by interpolation along a spectral axis, to generate hyperspectral images of plants. The transformation can be based on spectral bases formed from hyperspectral sample images including images of plant matter. Plant characteristics, such as plant health, may be predicted based on the hyperspectral image. Plant health may be predicted by comparing derivatives of reflectance values with respect to wavelength for a plant of a given image relative to a reference derivative based on a reference hyperspectral image. The derivatives may be compared by determining a regression loss.
G01N 21/3563 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solidsPreparation of samples therefor
13.
SYSTEMS AND METHODS FOR THE IMPROVED DETECTION OF PLANTS
Systems and methods for detecting plants in a sequence of images are provided. A plant is predicted to be in a detection region in an image and the plant is tracked across multiple images. A tracker retains a memory of the plants past position and updates a tracking region for each subsequent image based on the memory and the new images, thus using temporal information to augment detection performance. The plant can be substantially stationary and exhibit growth between images. Tracking substantially stationary plants can improve detection of the plant between images relative to detection alone. The tracking region can be updated based on the substantially stationary position of the plant, for instance by combining the tracking region with further predictions of plant position in subsequent images. Combining can involve determining a union.
A pesticidal mixture of an oil and an aliphatic acid is provided. In mixture, the aliphatic acid and oil are each effective to reduce the mixture's melting point (or viscosity transition point) to below the oil's and acid's melting points (or viscosity transition points). The mixture can be eutectic, such that the melting point of the mixture is less than both the oil melting point and the acid melting point. The oil can be a fatty acid ester, such as a wax ester. The aliphatic acid can be a fatty acid. Pesticidal mixtures comprising various natural pesticidal oils are disclosed, including neem oil, palm oil, coconut oil, and karanja oil.
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01P 15/00 - Biocides for specific purposes not provided for in groups
15.
SYSTEMS AND METHODS FOR PREDICTING SOIL PROPERTIES
Machine learning models for generating predictions of soil properties based on Raman spectral measurements are provided. The models can be trained on synthetic training data with associated synthetic Raman spectral measurements and ground truth training data with associated ground truth Raman spectral measurements. Systems and methods for training such machine learning models are also provided. Predictions may be facilitated by generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Soil properties may comprise sequestered soil carbon, nitrogen, phosphorous, potassium, and other constituents. Synthetic and ground-truth modes of training may differ in the number and type of input provided.
Machine learning models for generating predictions of sequestered soil carbon content are provided. The models can be trained on synthetic training data with associated synthetic spectral measurements and ground truth training data with associated ground truth spectral measurements. Systems and methods for training such machine learning models are also provided. Prediction of sequestered soil carbon may be facilitated by using Raman spectral measurements, generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Synthetic and ground-truth modes of training may differ in the number and type of input provided.
Machine learning models for generating predictions of sequestered soil carbon content are provided. The models can be trained on synthetic training data with associated synthetic spectral measurements and ground truth training data with associated ground truth spectral measurements. Systems and methods for training such machine learning models are also provided. Prediction of sequestered soil carbon may be facilitated by using Raman spectral measurements, generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Synthetic and ground-truth modes of training may differ in the number and type of input provided.
Compositions and methods for increasing the efficacy of pesticidal compositions are described herein, including synergistic pesticidal compositions and methods for delivery of pesticidal active ingredients. Some pesticidal compositions and methods as described are directed to compositions and methods for increasing the efficacy of insecticides, including ryanoid and ryanodine receptor modulator insecticide active ingredients. Methods for enhancing the activity of pesticidal active ingredients in pesticidal compositions in use are also described.
A01N 37/02 - Saturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 37/06 - Unsaturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 43/40 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with one nitrogen atom as the only ring hetero atom six-membered rings
A01N 43/713 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with four or more nitrogen atoms as the only ring hetero atoms
Enhanced pesticidal compositions as well as adjuvants and methods for enhancing pesticidal compositions are provided. Combinations of adjuvants and aliphatic acids which, in combination, can provide improved penetration of aliphatic acids and/or pesticidal active ingredients through waxy plant cuticles (e.g. leaf cuticles) are provided. The adjuvants may comprise non-ionic surfactants, such as ethoxylated fatty alcohols and/or alky aryl polyethoxylate. The aliphatic acids may comprise C4-C12 saturated or unsaturated aliphatic acids, such as trans-2-hexenoic acid, trans-3 -hexenoic acid, 5-hexenoic acid, and/or 3-nonenoic acid. The pesticidal active ingredients may comprise strobilurins, and/or azoles, such as pyraclostrobin, azoxystrobin, epoxiconazole, and/or prothioconazole.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
Compositions and methods for increasing the efficacy of pesticidal compositions are described herein, including synergistic pesticidal compositions and methods for delivery of pesticidal active ingredients. Some pesticidal compositions and methods as described are directed to compositions and methods for increasing the efficacy of insecticides, including ryanoid and ryanodine receptor modulator insecticide active ingredients. Methods for enhancing the activity of pesticidal active ingredients in pesticidal compositions in use are also described.
A01N 43/40 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with one nitrogen atom as the only ring hetero atom six-membered rings
A01N 37/02 - Saturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 37/06 - Unsaturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 43/713 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with four or more nitrogen atoms as the only ring hetero atoms
A salt formed by mixing one or more aliphatic acids with a volatile base may be combined with a pesticidal composition (and/or, in some cases, an adjuvant for a pesticidal composition). Adding the volatile base can improve solubility of the one or more aliphatic acids in at least some pesticidal compositions and/or adjuvants, better promote a target pH of the resulting pesticidal composition, and/or promote bioactivity of the one or more aliphatic acids post- application. The composition can be applied together with a pesticide, e.g. by spraying, to control plant pests.
A01N 25/22 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing ingredients stabilising the active ingredients
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01N 43/22 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with one or more oxygen or sulfur atoms as the only ring hetero atom with one hetero atom rings with more than six members
A01N 61/00 - Biocides, pest repellants or attractants, or plant growth regulators containing substances of unknown or undetermined composition, e.g. substances characterised only by the mode of action
A01N 37/06 - Unsaturated carboxylic acids or thio-analogues thereofDerivatives thereof
22.
COMPOSITIONS COMPRISING A SATURATED OR UNSATURATED ALIPHATIC ACID AND A NON-IONIC SURFACTANT FOR ENHANCING PENETRATION OF PESTICIDE COMPONENTS
Enhanced pesticidal compositions as well as adjuvants and methods for enhancing pesticidal compositions are provided. Combinations of adjuvants and aliphatic acids which, in combination, can provide improved penetration of aliphatic acids and/or pesticidal active ingredients through waxy plant cuticles (e.g. leaf cuticles) are provided. The adjuvants may comprise non-ionic surfactants, such as ethoxylated fatty alcohols and/or alky aryl polyethoxylate. The aliphatic acids may comprise C4-C12 saturated or unsaturated aliphatic acids, such as trans-2-hexenoic acid, trans-3 -hexenoic acid, 5-hexenoic acid, and/or 3-nonenoic acid. The pesticidal active ingredients may comprise strobilurins, and/or azoles, such as pyraclostrobin, azoxystrobin, epoxiconazole, and/or prothioconazole.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A computer system that predicts synergistic interactions between pesticidal and synergistic compounds of a pesticidal composition is described. The system provides a trained classifier that provides probabilistic predictions of the synergy between two or more compounds on a pest. The system may select features for transformation, encode them, generate one or more predictions, and combine the predictions. The predictions may be evaluated by experimental testing, e.g. in vitro or in planta, and/or used to formulate and/or apply a pesticidal composition.
Systems and methods for training machine learning models over labeled and unlabeled datasets are provided. Labels are assigned to unlabeled data by selecting a labeling approach, such as active learning or semi-supervised learning, based on uncertainty in the model's predictions. The selection of the labeling approach may be varied over the course of training, e.g. so that unlabeled dataset samples with progressively more uncertain predictions are pseudo-labeled via semi-supervised learning rather than with active learning, thereby reducing the load on the oracle and recognizing the increasing confidence in the model's overall calibration as training progresses.
Systems and methods for training machine learning models over labeled and unlabeled datasets are provided. Labels are assigned to unlabeled data by selecting a labeling approach, such as active learning or semi-supervised learning, based on uncertainty in the model's predictions. The selection of the labeling approach may be varied over the course of training, e.g. so that unlabeled dataset samples with progressively more uncertain predictions are pseudo-labeled via semi-supervised learning rather than with active learning, thereby reducing the load on the oracle and recognizing the increasing confidence in the model's overall calibration as training progresses.
Pesticidal compositions comprising a spinosyn active ingredient and a salicylate solvent operable to dissolve the spinosyn active ingredient are provided. The pesticidal composition may comprise an emulsion and/or an emulsifiable concentrate (EC) and may comprise an emulsifier. The pesticidal composition may comprise a carrier oil, such as a neutral oil, e.g. to limit potential phytotoxicity induced by the salicylate solvent. The pesticidal composition may comprise an oil- soluble saturated or unsaturated aliphatic acid dissolvable in the salicylate solvent and/or the carrier oil. The pesticidal composition may comprise one or more diluents, fragrance additives, ultraviolet light blockers, and/or other additives. The pesticidal composition may comprise a solution, emulsion, emulsifiable concentrate, and/or any other suitable composition of spinosyn active ingredient and salicylate solvent.
A01N 43/22 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with one or more oxygen or sulfur atoms as the only ring hetero atom with one hetero atom rings with more than six members
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
Pesticidal compositions comprising a spinosyn active ingredient and a salicylate solvent operable to dissolve the spinosyn active ingredient are provided. The pesticidal composition may comprise an emulsion and/or an emulsifiable concentrate (EC) and may comprise an emulsifier. The pesticidal composition may comprise a carrier oil, such as a neutral oil, e.g. to limit potential phytotoxicity induced by the salicylate solvent. The pesticidal composition may comprise an oil- soluble saturated or unsaturated aliphatic acid dissolvable in the salicylate solvent and/or the carrier oil. The pesticidal composition may comprise one or more diluents, fragrance additives, ultraviolet light blockers, and/or other additives. The pesticidal composition may comprise a solution, emulsion, emulsifiable concentrate, and/or any other suitable composition of spinosyn active ingredient and salicylate solvent.
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01N 43/22 - Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with one or more oxygen or sulfur atoms as the only ring hetero atom with one hetero atom rings with more than six members
Systems and methods for training a machine learning model for generating a structural representation of a plant are provided, as well as systems and methods for generating a structural representation of a plant via such a model. The training method involves encoding a plant image into a structural representation of the plant (e.g. a "skeleton"), decoding the structural representation of the plant into a reconstructed image of the plant, and classifying the reconstructed image as having been generated based on a ground-truth structural representation or output of the encoder. Such classification incentivizes the encoder to produce structural representations which do not "smuggle" texture information (e.g. appearance, such as color). Texture information may be separately represented. The encoder, once trained, may be used to generate structural representations from plant images without necessarily requiring decoding or classification.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Systems and methods for training a soil mapping model are provided, as well as systems and methods for generating soil maps with such trained soil mapping models. The soil mapping models predict soil characteristics (e.g. carbon content) across an area of interest. The training method can involve refining the parameters of a soil map model by estimating the soil mapping model's uncertainty at various locations across an area of interest, selecting relatively high- uncertainty areas, identifying sampling locations within those areas for further samples to be collected, and further training the parameters of the soil mapping model based on the newly-collected samples.
Systems and methods for training a machine learning model for generating a structural representation of a plant are provided, as well as systems and methods for generating a structural representation of a plant via such a model. The training method involves encoding a plant image into a structural representation of the plant (e.g. a "skeleton"), decoding the structural representation of the plant into a reconstructed image of the plant, and classifying the reconstructed image as having been generated based on a ground-truth structural representation or output of the encoder. Such classification incentivizes the encoder to produce structural representations which do not "smuggle" texture information (e.g. appearance, such as color). Texture information may be separately represented. The encoder, once trained, may be used to generate structural representations from plant images without necessarily requiring decoding or classification.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Systems and methods for training a soil mapping model are provided, as well as systems and methods for generating soil maps with such trained soil mapping models. The soil mapping models predict soil characteristics (e.g. carbon content) across an area of interest. The training method can involve refining the parameters of a soil map model by estimating the soil mapping model's uncertainty at various locations across an area of interest, selecting relatively high- uncertainty areas, identifying sampling locations within those areas for further samples to be collected, and further training the parameters of the soil mapping model based on the newly-collected samples.
Devices and methods and pesticidal and/or pest control compositions are disclosed for the control of pests using the vapors of a pesticidal and/or pest control composition. Compositions, devices, methods and vapor forming pesticidal compositions which have improved vapor forming characteristics and are desirable under certain regulatory structures are also disclosed. In some non-limiting examples, the pests are arthropods or nematodes, and more particularly may include bed bugs, fleas, lice, ticks, or the like.
Systems and methods for hyperspectral imaging of plants are provided. Multispectral images of plants are transformed, e.g. by interpolation along a spectral axis, to generate hyperspectral images of plants. The transformation can be based on spectral bases formed from hyperspectral sample images including images of plant matter. Plant characteristics, such as plant health, may be predicted based on the hyperspectral image. Plant health may be predicted by comparing derivatives of reflectance values with respect to wavelength for a plant of a given image relative to a reference derivative based on a reference hyperspectral image. The derivatives may be compared by determining a regression loss.
Systems and methods for hyperspectral imaging of plants are provided. Multispectral images of plants are transformed, e.g. by interpolation along a spectral axis, to generate hyperspectral images of plants. The transformation can be based on spectral bases formed from hyperspectral sample images including images of plant matter. Plant characteristics, such as plant health, may be predicted based on the hyperspectral image. Plant health may be predicted by comparing derivatives of reflectance values with respect to wavelength for a plant of a given image relative to a reference derivative based on a reference hyperspectral image. The derivatives may be compared by determining a regression loss.
Systems and methods for hyperspectral imaging of plants are provided. Multispectral images of plants are transformed, e.g. by interpolation along a spectral axis, to generate hyperspectral images of plants. The transformation can be based on spectral bases formed from hyperspectral sample images including images of plant matter. Plant characteristics, such as plant health, may be predicted based on the hyperspectral image. Plant health may be predicted by comparing derivatives of reflectance values with respect to wavelength for a plant of a given image relative to a reference derivative based on a reference hyperspectral image. The derivatives may be compared by determining a regression loss.
Systems and methods for hyperspectral imaging of plants are provided. Multispectral images of plants are transformed, e.g. by interpolation along a spectral axis, to generate hyperspectral images of plants. The transformation can be based on spectral bases formed from hyperspectral sample images including images of plant matter. Plant characteristics, such as plant health, may be predicted based on the hyperspectral image. Plant health may be predicted by comparing derivatives of reflectance values with respect to wavelength for a plant of a given image relative to a reference derivative based on a reference hyperspectral image. The derivatives may be compared by determining a regression loss.
Systems and methods for detecting plants in a sequence of images are provided. A plant is predicted to be in a detection region in an image and the plant is tracked across multiple images. A tracker retains a memory of the plant's past position and updates a tracking region for each subsequent image based on the memory and the new images, thus using temporal information to augment detection performance. The plant can be substantially stationary and exhibit growth between images. Tracking substantially stationary plants can improve detection of the plant between images relative to detection alone. The tracking region can be updated based on the substantially stationary position of the plant, for instance by combining the tracking region with further predictions of plant position in subsequent images. Combining can involve determining a union.
Systems and methods for detecting plants in a sequence of images are provided. A plant is predicted to be in a detection region in an image and the plant is tracked across multiple images. A tracker retains a memory of the plant's past position and updates a tracking region for each subsequent image based on the memory and the new images, thus using temporal information to augment detection performance. The plant can be substantially stationary and exhibit growth between images. Tracking substantially stationary plants can improve detection of the plant between images relative to detection alone. The tracking region can be updated based on the substantially stationary position of the plant, for instance by combining the tracking region with further predictions of plant position in subsequent images. Combining can involve determining a union.
Agricultural compositions comprising emulsifier systems for a pesticidal natural oil active ingredient are disclosed. One such composition includes a pesticidal natural oil active ingredient and an emulsifier system with a first component selected from glyceryl oleate, ethoxylated oleate, and ethoxylated soybean oil; and a second component comprising ethoxylated castor oil; wherein the ratio between the first component and said second component is between 1:3 and 3:1; and where the emulsifier system disperses the pesticidal natural oil active ingredient in a water emulsion. Methods for providing agricultural compositions and applications to control one or more pests are also disclosed.
A01N 65/00 - Biocides, pest repellants or attractants, or plant growth regulators containing material from algae, lichens, bryophyta, multi-cellular fungi or plants, or extracts thereof
40.
PESTICIDAL COMPOSITIONS WITH ENHANCED PHYSICAL CHARACTERISTICS
A pesticidal mixture of an oil and an aliphatic acid is provided. In mixture, the aliphatic acid and oil are each effective to reduce the mixture's melting point (or viscosity transition point) to below the oil's and acid's melting points (or viscosity transition points). The mixture can be eutectic, such that the melting point of the mixture is less than both the oil melting point and the acid melting point. The oil can be a fatty acid ester, such as a wax ester. The aliphatic acid can be a fatty acid. Pesticidal mixtures comprising various natural pesticidal oils are disclosed, including neem oil, palm oil, coconut oil, and karanja oil.
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01N 37/02 - Saturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 37/06 - Unsaturated carboxylic acids or thio-analogues thereofDerivatives thereof
41.
PESTICIDAL COMPOSITIONS WITH ENHANCED PHYSICAL CHARACTERISTICS
A pesticidal mixture of an oil and an aliphatic acid is provided. In mixture, the aliphatic acid and oil are each effective to reduce the mixture's melting point (or viscosity transition point) to below the oil's and acid's melting points (or viscosity transition points). The mixture can be eutectic, such that the melting point of the mixture is less than both the oil melting point and the acid melting point. The oil can be a fatty acid ester, such as a wax ester. The aliphatic acid can be a fatty acid. Pesticidal mixtures comprising various natural pesticidal oils are disclosed, including neem oil, palm oil, coconut oil, and karanja oil.
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01N 37/02 - Saturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 37/06 - Unsaturated carboxylic acids or thio-analogues thereofDerivatives thereof
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
42.
Pesticidal compositions with improved physical characteristics
Pesticidal compositions for improving physical characteristics of pesticide formulations which comprise natural pesticidal oil active ingredients are disclosed. One such composition comprises a pesticidal natural oil active ingredient, a surfactant to disperse the active ingredient in a water emulsion and a polymeric pour point depressant effective to reduce a pour point temperature of the pesticidal natural oil active ingredient. Methods for providing pesticidal compositions and application to control one or more pests are also disclosed.
A61K 36/00 - Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 27/00 - Biocides, pest repellants or attractants, or plant growth regulators containing hydrocarbons
A01N 31/16 - Oxygen or sulfur directly attached to an aromatic ring system with two or more oxygen or sulfur atoms directly attached to the same aromatic ring system
A computer system that predicts synergistic interactions between pesticidal and synergistic compounds of a pesticidal composition is described. The system provides a trained classifier that provides probabilistic predictions of the synergy between two or more compounds on a pest. The system may select features for transformation, encode them, generate one or more predictions, and combine the predictions. The predictions may be evaluated by experimental testing, e.g. in vitro or in planta, and/or used to formulate and/or apply a pesticidal composition.
A01N 61/00 - Biocides, pest repellants or attractants, or plant growth regulators containing substances of unknown or undetermined composition, e.g. substances characterised only by the mode of action
G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
G16C 20/00 - Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
A computer system that predicts synergistic interactions between pesticidal and synergistic compounds of a pesticidal composition is described. The system provides a trained classifier that provides probabilistic predictions of the synergy between two or more compounds on a pest. The system may select features for transformation, encode them, generate one or more predictions, and combine the predictions. The predictions may be evaluated by experimental testing, e.g. in vitro or in planta, and/or used to formulate and/or apply a pesticidal composition.
G16C 20/00 - Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
G16C 20/30 - Prediction of properties of chemical compounds, compositions or mixtures
G16C 20/70 - Machine learning, data mining or chemometrics
A01N 61/00 - Biocides, pest repellants or attractants, or plant growth regulators containing substances of unknown or undetermined composition, e.g. substances characterised only by the mode of action
Pesticidal compositions for improving physical characteristics of pesticide formulations which comprise natural pesticidal oil active ingredients are disclosed. One such composition comprises a pesticidal natural oil active ingredient, a surfactant to disperse the active ingredient in a water emulsion, a polymeric pour point depressant effective to reduce a pour point temperature of the pesticidal natural oil active ingredient and a hydrocarbon solvent. Methods for providing pesticidal compositions and application to control one or more pests are also disclosed.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 27/00 - Biocides, pest repellants or attractants, or plant growth regulators containing hydrocarbons
A01N 31/16 - Oxygen or sulfur directly attached to an aromatic ring system with two or more oxygen or sulfur atoms directly attached to the same aromatic ring system
Devices and methods and pesticidal and/or pest control compositions are disclosed for the control of pests using the vapors of a pesticidal and/or pest control composition. Compositions, devices, methods and vapor forming pesticidal compositions which have improved vapor forming characteristics and are desirable under certain regulatory structures are also disclosed. In some non-limiting examples, the pests are arthropods or nematodes, and more particularly may include bed bugs, fleas, lice, ticks, or the like.
Agricultural compositions comprising emulsifier systems for a pesticidal natural oil active ingredient are disclosed. One such composition includes a pesticidal natural oil active ingredient and an emulsifier system with a first component selected from glyceryl oleate, ethoxylated oleate, and ethoxylated soybean oil; and a second component comprising ethoxylated castor oil; wherein the ratio between the first component and said second component is between 1:3 and 3:1; and where the emulsifier system disperses the pesticidal natural oil active ingredient in a water emulsion. Methods for providing agricultural compositions and applications to control one or more pests are also disclosed.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
48.
EMULSIFIER SYSTEM FOR AGRICULTURAL COMPOSITIONS COMPRISING A PESTICIDAL NATURAL OIL
Agricultural compositions comprising emulsifier systems for a pesticidal natural oil active ingredient are disclosed. One such composition includes a pesticidal natural oil active ingredient and an emulsifier system with a first component selected from glyceryl oleate, ethoxylated oleate, and ethoxylated soybean oil; and a second component comprising ethoxylated castor oil; wherein the ratio between the first component and said second component is between 1:3 and 3:1; and where the emulsifier system disperses the pesticidal natural oil active ingredient in a water emulsion. Methods for providing agricultural compositions and applications to control one or more pests are also disclosed.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
49.
PESTICIDAL COMPOSITIONS WITH IMPROVED PHYSICAL CHARACTERISTICS
Pesticidal compositions for improving physical characteristics of pesticide formulations which comprise natural pesticidal oil active ingredients are disclosed. One such composition comprises a pesticidal natural oil active ingredient, a surfactant to disperse the active ingredient in a water emulsion and a polymeric pour point depressant effective to reduce a pour point temperature of the pesticidal natural oil active ingredient. Methods for providing pesticidal compositions and application to control one or more pests are also disclosed.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
Pesticidal compositions for improving physical characteristics of pesticide formulations which comprise natural pesticidal oil active ingredients are disclosed. One such composition comprises a pesticidal natural oil active ingredient, a surfactant to disperse the active ingredient in a water emulsion, a polymeric pour point depressant effective to reduce a pour point temperature of the pesticidal natural oil active ingredient and a hydrocarbon solvent. Methods for providing pesticidal compositions and application to control one or more pests are also disclosed.
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
Pesticidal compositions for improving physical characteristics of pesticide formulations which comprise natural pesticidal oil active ingredients are disclosed. One such composition comprises a pesticidal natural oil active ingredient, a surfactant to disperse the active ingredient in a water emulsion, a polymeric pour point depressant effective to reduce a pour point temperature of the pesticidal natural oil active ingredient and a hydrocarbon solvent. Methods for providing pesticidal compositions and application to control one or more pests are also disclosed.
A01N 25/02 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests containing liquids as carriers, diluents or solvents
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
52.
PESTICIDAL COMPOSITIONS WITH IMPROVED PHYSICAL CHARACTERISTICS
Pesticidal compositions for improving physical characteristics of pesticide formulations which comprise natural pesticidal oil active ingredients are disclosed. One such composition comprises a pesticidal natural oil active ingredient, a surfactant to disperse the active ingredient in a water emulsion and a polymeric pour point depressant effective to reduce a pour point temperature of the pesticidal natural oil active ingredient. Methods for providing pesticidal compositions and application to control one or more pests are also disclosed.
A01N 25/30 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests characterised by the surfactants
A01N 65/20 - Fabaceae or Leguminosae [Pea or Legume family], e.g. pea, lentil, soybean, clover, acacia, honey locust, derris or millettia
A01N 65/26 - Meliaceae [Chinaberry or Mahogany family], e.g. mahogany, langsat or neem
53.
CLEANING APPARATUS FOR REMOVING PESTS AND METHODS OF USING SAME
A cleaning apparatus engageable with a vacuum source is provided. The vacuum source can be used to draw fluid through an interior space of a housing of the cleaning apparatus through an inlet tube so that debris entrained in the fluid collects in a debris collection zone upstream of a downstream opening in the inlet tube. Methods of using the cleaning apparatus are provided.
A formulation is provided for application to a host plant to reduce, inhibit or impair one or more of growth and development of the host plant. A method of inhibiting growth plant growth and development is also provided as a means of controlling weedy species. The method comprises: selecting a suitable gene for growth suppression in a target plant; identifying an at least one target site accessible to base pairing in the suitable gene; identifying an at least one divergent site in the at least one target site; designing a construct complementary to the at least one divergent site; adding an at least one RNAi inducer to the construct; and delivering the construct to the target plant.
C12N 15/82 - Vectors or expression systems specially adapted for eukaryotic hosts for plant cells
A01N 61/00 - Biocides, pest repellants or attractants, or plant growth regulators containing substances of unknown or undetermined composition, e.g. substances characterised only by the mode of action
55.
Formulation and methods for control of weedy species
A formulation is provided for application to a host plant to reduce, inhibit or impair one or more of growth and development of the host plant. A method of inhibiting growth plant growth and development is also provided as a means of controlling weedy species. The method comprises: selecting a suitable gene for growth suppression in a target plant; identifying an at least one target site accessible to base pairing in the suitable gene; identifying an at least one divergent site in the at least one target site; designing a construct complementary to the at least one divergent site; adding an at least one RNAi inducer to the construct; and delivering the construct to the target plant.
C12N 15/82 - Vectors or expression systems specially adapted for eukaryotic hosts for plant cells
A01N 61/00 - Biocides, pest repellants or attractants, or plant growth regulators containing substances of unknown or undetermined composition, e.g. substances characterised only by the mode of action
56.
Formulations and methods for control of weedy species
A formulation is provided for application to a host plant to reduce, inhibit or impair one or more of growth and development of the host plant. A method of inhibiting growth plant growth and development is also provided as a means of controlling weedy species. The method comprises: selecting a suitable gene for growth suppression in a target plant; identifying an at least one target site accessible to base pairing in the suitable gene; identifying an at least one divergent site in the at least one target site; designing a construct complementary to the at least one divergent site; adding an at least one RNAi inducer to the construct; and delivering the construct to the target plant.
C12N 15/82 - Vectors or expression systems specially adapted for eukaryotic hosts for plant cells
A01N 61/00 - Biocides, pest repellants or attractants, or plant growth regulators containing substances of unknown or undetermined composition, e.g. substances characterised only by the mode of action
patents
