Patent classifications
A01D91/00
METHODS OF SEED TREATMENT AND RESULTING PRODUCTS
Provided herein are methods, compositions, and devices relating to administration of UV-B to a seed.
METHODS OF SEED TREATMENT AND RESULTING PRODUCTS
Provided herein are methods, compositions, and devices relating to administration of UV-B to a seed.
Systems and methods for post-harvest crop quality management
Embodiments of systems and approaches for managing post-harvest crop quality and pests are described. Such a system may include a plurality of edge devices each comprising sensor components and collectively forming a mesh network, for measuring the local physical environment within stored crops and, for example, transmitting the measurements to a service from within the crop storage area. In certain embodiments, such a system may be used to manage post-harvest crops and storage areasfor example, approaches are described for determining fumigation treatment duration, determining phosphine dosage, determining heat treatment duration, and determining safe storage time for crops.
Systems and methods for post-harvest crop quality management
Embodiments of systems and approaches for managing post-harvest crop quality and pests are described. Such a system may include a plurality of edge devices each comprising sensor components and collectively forming a mesh network, for measuring the local physical environment within stored crops and, for example, transmitting the measurements to a service from within the crop storage area. In certain embodiments, such a system may be used to manage post-harvest crops and storage areasfor example, approaches are described for determining fumigation treatment duration, determining phosphine dosage, determining heat treatment duration, and determining safe storage time for crops.
Prediction of in-field dry-down of a mature small grain, coarse grain, or oilseed crop using field-level analysis and forecasting of weather conditions and crop characteristics including sampled moisture content
A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.
Prediction of in-field dry-down of a mature small grain, coarse grain, or oilseed crop using field-level analysis and forecasting of weather conditions and crop characteristics including sampled moisture content
A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.
Modeling of time-variant threshability due to interactions between a crop in a field and atmospheric and soil conditions for prediction of daily opportunity windows for harvest operations using field-level diagnosis and prediction of weather conditions and observations and recent input of harvest condition states
A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.
Modeling of time-variant threshability due to interactions between a crop in a field and atmospheric and soil conditions for prediction of daily opportunity windows for harvest operations using field-level diagnosis and prediction of weather conditions and observations and recent input of harvest condition states
A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.
Nematode resistant crops
Methods of inhibiting plant parasitic nematodes, methods of obtaining transgenic plants useful for inhibiting such nematodes, and transgenic plants that are resistant to plant parasitic nematodes through inhibition of plant nematode CLAVATA3/ESR (CLE) peptide receptor genes are provided. Methods for expressing genes at plant parasitic nematode feeding sites with plant nematode CLE peptide receptor gene promoters are also provided, along with nematode CLE peptide receptor gene promoters that are useful for expressing genes in nematode feeding sites as well as transgenic plants and nematode resistant transgenic plants comprising the promoters.
Nematode resistant crops
Methods of inhibiting plant parasitic nematodes, methods of obtaining transgenic plants useful for inhibiting such nematodes, and transgenic plants that are resistant to plant parasitic nematodes through inhibition of plant nematode CLAVATA3/ESR (CLE) peptide receptor genes are provided. Methods for expressing genes at plant parasitic nematode feeding sites with plant nematode CLE peptide receptor gene promoters are also provided, along with nematode CLE peptide receptor gene promoters that are useful for expressing genes in nematode feeding sites as well as transgenic plants and nematode resistant transgenic plants comprising the promoters.