Abstract
Innovative methods in data collection and analytics for pest and disease management are advancing together with computational efficiency. Tools, such as the open-data kit, research electronic data capture, fall armyworm monitoring, and early warning- system application and remote sensing have aided the efficiency of all types of data collection, including text, location, images, audio, video, and others. Concurrently, data analytics have also evolved with the application of artificial intelligence and machine learning (ML) for early warning and decision-support systems. ML has repeatedly been used for the detection, diagnosis, modeling, and prediction of crop pests and diseases. This paper thus highlights the innovations, implications, and future progression of these technologies for sustainability.
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