Crop Disease Prediction and Associated Methods and Systems

A system comprising a hardware processor and non-transitory physical storage medium storing program code, the program code when executed by the hardware processor executes a process for predicting a disease related to a crop field of a crop, the program code comprising steps to:

• receive one or more ambient weather parameters related to environmental conditions around the crop field from an environmental sensor;
• determine a predicted disease risk indicator associated with the disease and the crop field based on the one or more ambient weather parameters, using a disease risk machine learning module trained on historical weather data from a weather database, wherein the disease risk machine learning module is a bidirectional long short-term memory (LSTM) neural network;
• receive a pathogen incidence indicator related to a presence of a pathogen around the crop field from a pathogen sensor, wherein the pathogen is associated with the disease, and wherein the pathogen sensor is a hardware device generating a measure of the pathogen incidence of the pathogen;
• and
• generate a disease score based on combining the predicted disease risk indicator and the pathogen incidence indicator, wherein the disease score represents a predicted severity of the disease around the crop field.