TomatoDetect: A ConvNet Powered Mobile Application for Detecting Tomato Leaf Diseases

Abstract:

Tomatoes are a mainstay in Nigerian cuisine, showing up in a variety of cuisines and offering several nutritional advantages like Vitamin C, potassium, and lycopene, which guard against cancer and heart disease. Nigeria now produces 10.8% of all fresh tomato production on the continent, placing it second overall. To avoid the enormous loss, extra care must be taken with the vulnerable crop tomato plant because of many infections that affect it. Farmers and other agricultural experts undergo laborious and time consuming procedures when visually checking crops that they think to be impacted by various infections in the real world, which does not ensure proper recognition and classification of particular plant diseases. To identify healthy tomato leaves and nine tomato leaf infections, this study created a mobile application. This study created two pre trained VGG 16 Convolutional Neural Networks (CNN or ConvNet) models using the Keras deep learning framework. With an accuracy of 96.51%, the model trained on the enhanced data surpassed the model trained without the augmented data. To accurately detect specific diseases and categorize healthy leaves in a real world scenario in tomato leaves, this DL model was chosen and used in a built mobile application. The chosen VGG 16 pre trained model was first transformed into a TensorFlowLite (TFLite) model applicable in an android mobile application before being deployed into a mobile application environment. The mechanism of gathering user data and transmitting it through the backend to be verified with the firebase database, which manages the application's storage and authentication, was designed using the kotlin programming language. With this mobile app in their hands, tomato farmers can detect disease outbreaks and spread in tomato leaves before they run out of control and endanger food security.

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