Plant Disease Identification and Recommendation of Organic Pesticides Using Machine Learning Techniques

Plant ailment identification is the biggest job for farmers in recent times. Over the years, plant and leaf diseases have caused an overall yield loss of 14% globally. Misdiagnosis of plant leaf diseases can lead to misuse of chemical pesticides, leading to economic burden to farmers and also causing soil depletion. Currently, plant leaf disease detection requires experts to diagnose and recommend pesticides, which is a time-consuming and costly process. Hence, our work aims at suggesting a machine learning technique that is reasonably useful than the existing manual method. We propose a software prototype and natural insecticides to avoid banana plant infection in our proposed work. Our work makes use convolution neural network to detect diseases in sunflower and banana plants. The proposed work was able to identify sigatoka leaf spot, Panama wilt in banana leaves and leaf blight, and downy mildew in sunflower leaves. The work carried out gave an overall accuracy of 94% in identifying and classifying the disease. Organic and chemical pesticides were suggested to control the identified disease