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.