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 assessing a field of plants for irrigation having program code to acquire at least one thermal image of a field having a plurality of plants thereon; generate a thermal map of the field from the thermal image providing a thermal value for a plurality of geographically referenced locations in the field; measure at least one weather parameter for the field; calculate a water stress value for each of the plurality of locations using the weather parameter and the thermal value; and estimate a water property value for each of the plurality of locations using the water stress value and a predetermined correlation of the water stress value and the water property value. Thus, the system leverages high-resolution aerial thermal imagery to estimate plant stress with the advantage of obtaining estimates of the water property value, such as the stem water potential (SWP), potentially for every plant in a field, thereby providing a more powerful tool for irrigation scheduling.

Abstract: A method of estimating a water property value for plants using aerial thermal images is provided. The thermal image values and at least one weather parameter are used to calculate a water stress value. The water property value is estimated using the water stress value and a predetermined correlation between the water stress value and the water property value.