Assessment of three Artificial Intelligence models performance in the detection of bacterial spot on tomato leaves in mozambique

Abstract

The agricultural sector is pivotal to Mozambique's economy through domestic consumption and exports of crops such as (Lycopersicon esculentum Mill.). However, tomato production faces several challenges, one of the most important being the bacterial diseases infestation. Artificial Intelligence (AI) has emerged as a transformative tool across industries, including agriculture, leveraging machine learning and extensive datasets to enhance productivity and sustainability. In a study conducted at Eduardo Mondlane University experimental trial station in Maputo, images of tomato leaves were collected and categorized to develop AI models for detection of bacterial spot disease. Data preprocessing included resizing and augmentation techniques to optimize model performance. Training involved Convolutional Neural Networks (CNNs) using TensorFlow and Adam optimizers. Among models tested (Resnet 50, Inception V3, VGG 16). The VGG 16 with data augmentation achieved the highest validation accuracy of 93.33% and minimal loss (0.1595%), demonstrating superior precision and generalization. This underscores the critical role of data augmentation in training robust AI models for effective Bacterial Spot detection in tomatoes, offering significant potential for improving agricultural outcomes in Mozambique.