Leveraging MobileNetV3 for In-Field Tomato Disease Detection in Malawi via CNN

Abstract

Malawi’s economy heavily depends on agriculture, including both commercial and subsistence farming. Smallholder and small-medium enterprises leading the production of tomatoes in Malawi cannot satisfy local demand due to problems such as pests, diseases, unstable markets, and high costs. Many farmers lack the expertise to effectively manage these threats. To address the problem of tomato leaf disease identification, this research aimed to develop an automated system for tomato leaf disease detection by utilizing data augmentation techniques, MobileNetV3, and Convolutional Neural Network algorithms. We trained models on secondary data collected from the public PlantVillage dataset and tested the resultant classifiers on primary data of local farm images. The experimental results demonstrate that both models tested better on the PlantVillage dataset. Additionally, with an accuracy of 92.59% and a loss of 0.2805, the pre-trained MobileNetV3 model conventionally performs better than a CNN model. However, when tested on the primary field dataset, the models did not meet expectations for generalization, with the pre-trained MobileNetV3 achieving an accuracy of 9.2%, and loss of 12.91 and the CNN achieving an accuracy of 10.14%, and loss of 8.11. The experiments aided in showing that the models trained on the PlantVillage dataset are not as effective when applied in real-world scenarios. Further improvements are needed to enhance the models’ generalization in real-world scenarios.