Potato Crop Disease Detection using Smart Tools

Crop infections have grown significantly in recent years due to severe climate change and a lack of crop immunity. Considerable research has taken place in the area of potato disease identification, primarily because of the effect diseases like early and late blight have on crop yield and quality. Most recent developments tap into deep learning & ML-based techniques, especially CNNs, Bagging & Boosting, to identify symptoms on the leaves potato crops using images. Such approaches quickly make findings without one having to personally examine the leaves, a plus for timely intervention by farmers. Some of these methods incorporate hyperspectral imaging and multi-spectral analysis to enhance accuracy by identifying more than one disease indicator. More generally, these AI-based methods contribute towards ensuring sustainable agriculture as they facilitate the use of reduced chemicals and ensure food security in terms of early and accurate disease management.