UrbanAgro: Utilizing advanced deep learning to support Sri Lankan urban farmers to detect and control common diseases in tomato plants

ABSTRACT

Plant diseases cause crucial damages and losses in crops around the entire world. Proper measures should be introduced on identifying plant diseases to prevent damages and minimize losses. With Covid-19 lockdowns, many urban dwellers are encouraged to grow their own foods. As most urban farmers do not tend to use pesticides in their farms, there is a high chance for the crops to get caught of various diseases. For the early detection of diseases on plants, different intelligence farming approaches such as machine learning and computer vision have been researched. The system proposed presents a practical, applicable solution for the identification of the type and location of five different types of leaves that include four types of diseased leaves and the healthy leaves of tomato plant, which is a significant difference from the conventional methods for plant disease classification. In this context, we have used YOLOv3 model that is a method based on transfer learning to diagnose tomato plant diseases using images taken in-place by camera devices on smartphones instead of users going through the procedure to collect, test, and analyze physical samples (leaves and plants) in the laboratory. The trained model achieved an average accuracy of 92%, which is exceptional in comparison to previous studies in this context. The target group of users is urban farmers who require a quick diagnosis on common tomato leaf diseases at any time of the day as they lack knowledge on diseases that are attached to plants. © 2022 Elsevier Inc. All rights reserved.