Abstract: Systems and methods for predicting pest susceptibility, comprising steps to receive geocoded geospatial image data of a crop field from sensors, receive microclimate data of the crop field, determine a degree of canopy closure for one or more portions of the crop field, and then generate a pest susceptibility index utilizing a risk model based on the degree of canopy closure and the microclimate data. The pest susceptibility index comprises a measure of a susceptibility of a crop in the crop field to one or more crop pests. In some embodiments, the method also comprises steps to generate a treatment plan (e.g., pesticide application) and to estimate an anticipated return on investment (ROI). The system therefore leverages remote-sensed data, machine data, analytics, and machine learning to enable farmers to predict, prevent, and control the outbreak of crop pests to greatest economic effect. Such a system addresses a fundamental problem in agriculture.

Abstract: Systems and methods for predicting pest susceptibility, comprising steps to receive geocoded geospatial image data of a crop field from sensors, receive microclimate data of the crop field, determine a degree of canopy closure for one or more portions of the crop field, and then generate a pest susceptibility index utilizing a risk model based on the degree of canopy closure and the microclimate data. The pest susceptibility index comprises a measure of a susceptibility of a crop in the crop field to one or more crop pests. In some embodiments, the method also comprises steps to generate a treatment plan (e.g., pesticide application) and to estimate an anticipated return on investment (ROI). The system therefore leverages remote-sensed data, machine data, analytics, and machine learning to enable farmers to predict, prevent, and control the outbreak of crop pests to greatest economic effect. Such a system addresses a fundamental problem in agriculture.

Abstract: Systems and methods for predicting pest susceptibility, comprising steps to receive geocoded geospatial image data of a crop field from sensors, receive microclimate data of the crop field, determine a crop vigor map for the crop field, and then generate a pest susceptibility map utilizing a risk model based on the crop vigor map and the microclimate data. The pest susceptibility map comprises a measure of a susceptibility of a crop in the crop field to one or more crop pests at one or more locations. In some embodiments, the method also comprises steps to generate a treatment plan (e.g., pesticide application) and to estimate an anticipated return on investment (ROI). The system therefore leverages remote-sensed data, machine data, analytics, and machine learning to enable farmers to predict, prevent, and control the outbreak of crop pests to greatest economic effect. Such a system addresses a fundamental problem in agriculture.

Abstract: Systems and methods for predicting pest susceptibility, comprising steps to receive geocoded geospatial image data of a crop field from sensors, receive microclimate data of the crop field, determine a crop vigor map for the crop field, and then generate a pest susceptibility map utilizing a risk model based on the crop vigor map and the microclimate data. The pest susceptibility map comprises a measure of a susceptibility of a crop in the crop field to one or more crop pests at one or more locations. In some embodiments, the method also comprises steps to generate a treatment plan (e.g., pesticide application) and to estimate an anticipated return on investment (ROI). The system therefore leverages remote-sensed data, machine data, analytics, and machine learning to enable farmers to predict, prevent, and control the outbreak of crop pests to greatest economic effect. Such a system addresses a fundamental problem in agriculture.