TOMATODETECT: A MOBILE APPLICATION FOR DETECTING TOMATO LEAF DISEASES BASED ON VGG-16 CONVNET

ABSTRACT

Tomatoes are a staple in Nigerian food, appearing in a wide range of recipes and providing several nutritional benefits such as Vitamin C, potassium, and lycopene, which help prevent heart disease and cancer. Nigeria is currently Africa's second-largest producer of fresh tomatoes, accounting for 10.8% of the continent's total. However, several diseases that infect the tomato plant have made it an endangered crop that needs special attention to reduce the massive loss of farmland. Farmers and other agriculture specialists go through tedious and time-consuming processes in visually inspecting crops that they suspect to be affected by various diseases in the real world, which does not guarantee accurate recognition and classification of specific plant diseases. Therefore, this study developed a mobile application to detect nine tomato leaf diseases and healthy tomato leaves. The Keras deep learning framework was used to develop two pre-trained VGG-16 Convolutional Neural Networks (CNN or ConvNet) models. The model trained on the augmented data outperformed the model trained without augmented data, with an accuracy of 96.51%. Consequently, this DL model was selected and deployed in a developed mobile application that can accurately detect specific diseases and classify healthy leaves in a real-world scenario in tomato leaves. The selected VGG-16 pre-trained model was deployed into a mobile application environment by first converting it into a TensorFlowLite (TFLite) model adaptable in an android mobile application. To develop the mobile application, the kotlin programming language was used to design the logic of collecting data from users and sending them through the backend for verification with the firebase database, which handles the application’s storage and authentication. With this mobile application in the hands of tomato farmers, the outbreak and spread of diseases in tomato leaves can be detected early and prevented from becoming uncontrollable and threatening food security.

Keywords: Android Application, Convolutional Neural Networks, Mobile Application, TensorFlow, Tomato Diseases, VGG-16