Detection of Tomato Diseases using EfficientNetB3: A Robust Deep Learning Method

Although tomato yield is a major factor in world agriculture, it is quite vulnerable to certain diseases that could substantially lower crop quality and output. Early and accurate detection of these diseases determines both food security protection and good crop management. Conventional methods of diagnosing diseases include specialist eye tests, which are time-consuming, prone to human mistake labour-intensive. Deep learning methods allow exact and automatic plant disease identification, thereby offering a good substitute for more conventional methods. This work aims to build an improved tomato disease detection system leveraging the EfficientNetB3 model, distinguished for its exceptional accuracy and computational economy. The outcomes of this work show the scalability and robustness of EfficientNetB3 as a tomato disease detecting tool. Farmers and other agricultural experts can gain from fast and precise disease diagnosis by including this model into their operations, therefore allowing them to take suitable action to reduce crop losses and increase output. At end, based on the EfficientNetB3 model, our work offers a fast and effective deep learning based method for tomato disease identification. The ability of this model to precisely diagnose several tomato diseases with little computational resources highlights its likely use in pragmatic precision agriculture.