Graph Intelligence‐enabled Precision Agriculture

Le résumé fourni par la source

Diseases in sugarcane greatly affect the overall agricultural output in the world, leading to large-scale losses. A novel disease prediction system, which is based on the use of advanced image processing methods and deep learning algorithms, is presented in this chapter. Chandraleka and Selvaraj (2025) proposed a new AI format to detect crop health in the early stages of precision agriculture setting, whereas Li et al. (2026) improved pest detection models using pyramid attention networks that made it possible to fuse multiple feature dimensions, especially honeysuckle pest detection. Future generation systems where multi-modal sensing and autonomous imaging systems are included will have more potential to add to the agricultural disease management systems. The improved sugarcane disease predictive system is a valuable step toward using modern technology in the improvement of sustainable agri-business, and it could be generalized in both disease prediction in support of more crop species and its overall application to AI-enhanced farming.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.