Multiclass Classification of Tomato Leaf Diseases Using Convolutional Neural Networks and Transfer Learning

Abstract

Tomato (biological name: Solanum lycopersicum) is an important food crop worldwide. However, due to climatic changes and various diseases, the yield of tomatoes decreased significantly, being detrimental from an economic point of view. Various diseases infect the tomato leaves, such as bacterial and septorial leaf spots, early blight and mosaic virus, to name a few. If uncared, these tomato leaf diseases (TLDs) can spread to other leaves and the fruit. Hence it is vital to detect these diseases as early as possible. Leaf examination is one of the standard techniques to identify and control the spread of diseases. Big Data has made substantial progress, and with the help of computer vision and deep learning techniques to analyse data, we can identify the diseased leaves and help control the disease's spread further. This study used three lightweight midgeneration convolutional neural networks (CNNs) classification network architectures which has the scope to be deployed in IoT devices to help the agricultural community tackle TLDs. It also shows the efficacy of the models with and without geometric data augmentation. The model was trained on a Kaggle data set containing a more significant number of samples to make a robust model aware of broader data distribution and validated on the Plant Village dataset to test its efficacy. The results show that applying transfer learning using ImageNet weights to the MobileNet Architecture using geometrically augmented sample images yields a train and test accuracy of 99.71% and 99.49%, respectively.