Advances in data-collection tools and analytics for crop pest and disease management

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.

Automated summary

Innovative methods in data collection and analytics, along with improved computational efficiency, are advancing pest and disease management. Tools such as open-data kits, research electronic data capture, and early warning-system applications have improved the efficiency of collecting various types of data. Additionally, the application of artificial intelligence and machine learning has contributed to the development of data analytics for the prediction and decision support of crop pests and diseases.