Plant Disease Detection and Treatment Recommendation System using Convolutional Neural Networks

Feeding the population of the world is important to agriculture - losses in crops due to diseases ultimately result in loss of yield and quality. To minimize losses from commodity losses due to crop diseases, an early (and correctly) identification of disease must occur to allow sustainable agricultural systems to grow in a sustainable manner. The traditional means of detecting plant diseases by way of visual inspection by a specialist is somewhat limited in its efficiency because it takes a significant amount of time to complete, it is subjective and is often a difficult process for an average farm to complete successfully. This article discusses the development of an intelligent system that utilizes Machine Learning (particularly Deep Learning) to identify and recommend a solution to the user of detected plant disease. The system will incorporate the use of an EfficientNet-based CNN Architecture that was trained with the PlantVillage database containing images of 38 different classes of plant leaf images created by plant scientists and plant specialists. The system will allow the user to submit an image of their plant leaf to the system which will provide the user with an answer as to whether their leaf has a disease, list of recommended treatments, prevention methods and links to where they may be able to purchase the remedies for their trees. Overall, the proposed system will provide a valuable tool for smart agriculture decision making by providing the user with an early identification of disease, assistance with developing an action plan for treatment and continuing education.