A Review on Machine Learning Based Techniques for Leaf Blight Detection

 A Review on Machine Learning Based Techniques for Leaf Blight Detection

Shirin Mansuri1, Prof. Pankaj Raghuwanshi2

Abstract— Plant diseases like early and late blight must be promptly identified, and this requires automated blight detection. These illnesses have the potential to spread quickly and seriously harm crops. By taking timely and focused action to limit the effects, farmers can reduce crop losses and ensure food security through early detection. If left untreated, blight diseases can cause significant output losses in crops, especially in staple items like tomatoes and potatoes. Advanced technologies like machine learning and image analysis enable automated detection systems to swiftly and precisely identify disease symptoms, facilitating early intervention to prevent or minimise crop losses. Therefore, the pressing need to address the problems caused by plant diseases, encourage sustainable agricultural methods, and support international efforts to ensure food security is what motivates the need for automated blight detection. Farmers, customers, and the environment all stand to gain from the increased efficiency and precision of disease management brought about by the integration of new technologies in agriculture. This paper presents a review on the state of the art image processing and machine learning, deep learning based approaches for detection of potato leaf blight disease.

Keywords— Potato Leaf Disease (blight), Deep Learning, Convolutional Neural Network, Classification Accuracy.