AI and computer vision-based pest and disease detection for greenhouse crops in Zimbabwe

Abstract

Pests and diseases remain a major constraint to agricultural productivity in Zimbabwe, causing significant losses and limiting farmers’ income. In greenhouse farming, early detection of infestations is critical to ensure healthy crops and reduce pesticide overuse. This study presents a computer-vision system powered by artificial intelligence (AI) for early identification of common pests and diseases in greenhouse crops. A convolutional neural network (CNN) trained on a regionally curated dataset of healthy and diseased plant images automatically classifies visual symptoms from digital photographs. The system is implemented through a MATLAB desktop application, enabling offline classification for users with limited internet access. The CNN achieved 83 % training accuracy and 82 % validation accuracy, with high precision and recall across multiple crop categories. Testing confirmed reliable detection of leaf curl, septoria leaf spot, and related infections in tomatoes and peppers. This work demonstrates that locally trained deep-learning models can effectively support greenhouse farmers in Zimbabwe, enhancing early response and minimizing losses due to pest and disease outbreaks.