Abrégé

A method according to an embodiment includes receiving, at one or more processors, satellite image data, temperature measurement data, precipitation measurement data, and one or more agronomic parameters, associated with an agricultural land segment. The method also includes predicting, using the one or more processors and a first neural network, a biomass value associated with the agricultural land segment. The method also includes predicting, using the one or more processors and a second neural network, a sugar content value associated with the agricultural land segment. The method also includes predicting, using the one or more processors, a total sugar value associated with the agricultural land segment. Optionally, the method also includes optimizing harvest dates for a plurality of agricultural land segments that includes the agricultural land segment, based on one or more constraints.

Classes IPC  ?

• A01B 79/00 - Méthodes de travail de la terre
• A01B 79/02 - Méthodes de travail de la terre en combinaison avec d'autres opérations agricoles, p. ex. fertilisation, plantation
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/10 - Scènes terrestres
• G06V 20/13 - Images satellite

Abrégé

A method according to an embodiment includes receiving, at one or more processors, satellite image data, temperature measurement data, precipitation measurement data, and one or more agronomic parameters, associated with an agricultural land segment. The method also includes predicting, using the one or more processors and a first neural network, a biomass value associated with the agricultural land segment. The method also includes predicting, using the one or more processors and a second neural network, a sugar content value associated with the agricultural land segment. The method also includes predicting, using the one or more processors, a total sugar value associated with the agricultural land segment. Optionally, the method also includes optimizing harvest dates for a plurality of agricultural land segments that includes the agricultural land segment, based on one or more constraints.

Classes IPC  ?

• A01B 79/00 - Méthodes de travail de la terre
• A01B 79/02 - Méthodes de travail de la terre en combinaison avec d'autres opérations agricoles, p. ex. fertilisation, plantation
• G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/10 - Scènes terrestres
• G06V 20/13 - Images satellite

Abrégé

The present invention relates to a method of identifying a soil-borne pathogen on a target crop in an agricultural parcel, comprising the steps of: obtaining a first digital image of the agricultural parcel in a first crop cycle, wherein in the first crop cycle the target crop is grown in the agricultural parcel; obtaining a reference digital image of the agricultural parcel in a reference crop cycle, wherein in the reference crop cycle a reference crop is grown in the agricultural parcel and wherein the reference crop is different from the target crop; computing a first vegetation index related to a first pixel in the first digital image, determining a first signed distance between the first pixel and surrounding pixels thereof based on the first vegetation index, and detecting a first anomaly of the first pixel if the first signed distance is below a predefined threshold; defining a reference anomaly for a reference pixel of the reference digital image; and identifying the soil-borne pathogen of the first pixel in case the first anomaly does not match the reference anomaly.

Classes IPC  ?

• G06T 7/00 - Analyse d'image

Abrégé

A system for computational imaging spectroscopy to provide compact and lightweight design, as well as large field of view of an object to be captured. The system includes imaging components, and computational device. The imaging components includes lens assembly, a fixed or variable-diameter aperture, spectral filter array and imaging sensor. The lens assembly provides wide angle of view, image-side telecentricity, and further may correct for longitudinal chromatic aberrations. The lens assembly may not provide correction of lateral chromatic aberrations. Furthermore, the lens assembly provides image-space telecentricity so as to chief rays are incident perpendicular to image sensor. The lens assembly may produce different chromatic aberrations pattern for each wavelength within the spectral range of interest. The pass-band nanofilter array is configured to filter a plurality of specific bands of light reflected from the imaged object and further produces a plurality of spatio-spectral samples of the imaged object projected onto the photosensitive pixels of imaging sensor. The computational device reconstructs complete spectral cube within the spectral range of interest, and further enables the computation of object reflectance at each pixel of the captured image from the plurality of spatio-spectral samples registered by the imaging sensor.

Classes IPC  ?

• G01J 3/26 - Production du spectreMonochromateurs en utilisant une réflexion multiple, p. ex. interféromètre de Fabry-Perot, filtre à interférences variables
• G01J 3/28 - Étude du spectre
• G01J 3/02 - SpectrométrieSpectrophotométrieMonochromateursMesure de la couleur Parties constitutives

Abrégé

A plant system (100), comprising a plurality of plants (102) in a substrate (101); a mycorrhizal fungal community (104) in the substrate arranged to form a biological interface with roots of the plants enabling an exchange of chemical substances between the fungus and the plurality of plants; and at least a sensor (207), which interfaces with said mycorrhizal fungal community and is configured to collect sensory information about a physiological condition and phenotypic state of said plurality of plants.

Classes IPC  ?

• A01G 18/10 - MycorhizeAssociations de mycorhize

Abrégé

A system for computational imaging spectroscopy to provide compact and lightweight design, as well as large field of view of an object to be captured. The system includes imaging components, and computational device. The imaging components includes lens assembly, a fixed or variable-diameter aperture, spectral filter array and imaging sensor. The lens assembly provides wide angle of view, image-side telecentricity, and further may correct for longitudinal chromatic aberrations. The lens assembly may not provide correction of lateral chromatic aberrations. Furthermore, the lens assembly provides image-space telecentricity so as to chief rays are incident perpendicular to image sensor. The lens assembly may produce different chromatic aberrations pattern for each wavelength within the spectral range of interest. The pass-band nanofilter array is configured to filter a plurality of specific bands of light reflected from the imaged object and further produces a plurality of spatio-spectral samples of the imaged object projected onto the photosensitive pixels of imaging sensor. The computational device reconstructs complete spectral cube within the spectral range of interest, and further enables the computation of object reflectance at each pixel of the captured image from the plurality of spatio-spectral samples registered by the imaging sensor.

Classes IPC  ?

• B82Y 20/00 - Nano-optique, p. ex. optique quantique ou cristaux photoniques
• G01J 3/02 - SpectrométrieSpectrophotométrieMonochromateursMesure de la couleur Parties constitutives
• G01J 3/28 - Étude du spectre
• G01J 3/36 - Étude de plusieurs bandes d’un spectre à l’aide de détecteurs distincts
• G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,
• G02B 13/00 - Objectifs optiques spécialement conçus pour les emplois spécifiés ci-dessous
• G02B 13/14 - Objectifs optiques spécialement conçus pour les emplois spécifiés ci-dessous à utiliser avec des radiations infrarouges ou ultraviolettes

Abrégé

The present disclosure relates to methods and devices for a systemic approach to plant-based ecosystem management, including for juxtaposing, processing, organising, and visualizing data relevant to plant- based ecosystems, such as agricultural ecosystems, and delineating external interventions into such systems, such as human interventions, including those with automated machines. Recognizing the time-based— for example, seasonal—nature of plant-based ecosystems, this invention ) juxtaposes relevant—but often previously dispersed—data types, 2) organizes them in tensors of customizable dimensions so as to facilitate modeling and in particular deep neural network and other deep machine learning and artificial intelligence approaches that take into account time- based, or other variable-based, changes to identify areas of interest within given land parcels, and 3) visualizes the data so as to highlight time-based, or other variable-based, relationships and trends. Such steps facilitate the development of individual plant or sub-land-parcel prescriptions for human intervention aimed at optimizing ecosystem output traits in the current season while considering their impact on subsequent seasons, thereby enabling the systematic management of plant-based ecosystems.

Classes IPC  ?

• G06Q 10/00 - AdministrationGestion