Automatic Early Detection of Tomato Leaf Disease using IoT and Deep Learning

Abstract

Tomato plants are defenseless to different illnesses, including bacterial, contagious, and viral contaminations, which can fundamentally lessen crop yield and quality on the off chance that not identified and treated early. Farmers may experience increased crop damage and financial losses as a result of this detection delay. The goal is to foster a robotized framework utilizing IoT (Internet of Things) gadgets, for example, cameras conveyed in the field, joined with profound learning strategies, to precisely and quickly distinguish illnesses in tomato plants. This framework intends to give ranchers an early admonition framework that can recognize and order infections quickly, empowering convenient intercession and designated treatment, accordingly further developing harvest wellbeing and yield. Profound learning has essentially expanded the precision of picture classification and article identification frameworks' acknowledgment as of late. The exploration zeroed in on computerizing the early location of tomato leaf sicknesses utilizing IoT innovation and a changed ResNet50 profound learning model. At first, IoT gadgets, including sensors and cameras, were conveyed in tomato fields to gather plant-related information and pictures. We focused on calibrating the hyper boundaries of pre-prepared models, including GoogLeNet, SquezeNet and ResNet-50. The notable Tomato leaf disease detection dataset, which incorporates 3,890 picture tests of different sickness and healthy leaves, was utilized for the tests. Using comparable cutting-edge research, a comparative analysis was also conducted. The tests showed that ResNet-50 outflanked cutting edge models with a 99.87% more prominent characterization exactness. The framework demonstrated commendable capability in identifying whether tomato plant leaves were affected by disease in their early stages. This capability enabled farmers to receive timely alerts through mobile application, allowing for more effective management of the issue.