Abstract: A deep learning system is used to predict crop characteristics from inputs that include crop variety features, environmental features, and field management features. The deep learning system includes domain-specific modules for each category of features. Some of the domain-specific modules are implemented as convolutional neural networks (CNN) while others are implemented as fully-connected neural networks. Interactions between different domains are captured with cross attention between respective embeddings. Embeddings from the multiple domain-specific modules are concatenated to create a deep neural network (DNN). The prediction generated by the DNN is a characteristic of the crop such as yield, height, or disease resistance. The DNN can be used to select a crop variety for planting in a field. For a crop that is planted, the DNN may be used to select a field management technique.

Abstract: A deep learning system is used to predict crop characteristics from inputs that include crop variety features, environmental features, and field management features. The deep learning system includes domain-specific modules for each category of features. Some of the domain-specific modules are implemented as convolutional neural networks (CNN) while others are implemented as fully-connected neural networks. Interactions between different domains are captured with cross attention between respective embeddings. Embeddings from the multiple domain-specific modules are concatenated to create a deep neural network (DNN). The prediction generated by the DNN is a characteristic of the crop such as yield, height, or disease resistance. The DNN can be used to select a crop variety for planting in a field. For a crop that is planted, the DNN may be used to select a field management technique.

Abstract: Methods and compositions for identifying novel genes useful for modulating desired agronomic traits in plants are presented herein. The present disclosure relates to methods for identifying line-specific and cluster-specific genes from plants that show perturbation of expression in response to perturbation of expression of a primary gene, and the perturbation of expression of the line-specific or cluster-specific gene confers alterations in agronomic characteristics upon the plant.

Abstract: A system includes reception of first multi-spectral reflectance data at a first input of a trained neural network, reception of weather data at a second input of the trained neural network, and generation, using the artificial neural network, of second multi-spectral reflectance data based on the received first multi-spectral reflectance data and the received weather data.

Abstract: Methods and compositions for identifying novel genes useful for modulating desired agronomic traits in plants are presented herein. The present disclosure relates to methods for identifying line-specific and cluster-specific genes from plants that show perturbation of expression in response to perturbation of expression of a primary gene, and the perturbation of expression of the line-specific or cluster-specific gene confers alterations in agronomic characteristics upon the plant.