Application of MobileNets Convolutional Neural Network Model in Detecting Tomato Late Blight Disease

Authors

Richard C Rajabu, Mbeya University of Science and Technology, College of Information Communication Technology, Department of Computer Science and Engineering, Mbeya, Tanzania.Follow
Juma S Ally, Mbeya University of Science and Technology, College of Information Communication Technology, Department of Electronics and Telecommunication, P.O. Box 131, Mbeya, Tanzania.Follow
Jamal F Banzi, Sokoine University of Agriculture, Department of Tourism and Recreation, Morogoro, Tanzania.Follow

Section

Mathematics and Computational Sciences

Abstract

Late blight (LB) disease causes significant annual losses in tomato production. Early identification of this disease is crucial in halting its severity. This study aimed to leverage the strength of Convolutional Neural Networks (CNNs) in automated prediction of tomato LB. Through transfer learning, the MobileNetV3 model was trained on high-quality, well-labeled images from Kaggle datasets. The trained model was tested on different images of healthy and infected leaves taken from different real-world locations in Mbeya, Arusha, and Morogoro. Test results demonstrated the model's success in identifying LB disease, with an accuracy of 81% and a precision of 76%. The trained model has the potential to be integrated into an offline mobile app for real-time use, improving the efficiency and effectiveness of LB disease detection in tomato production. Similar methods could also be applied to detect other tomato infections.

Recommended Citation

Rajabu, Richard C; Ally, Juma S; and Banzi, Jamal F (2022) "Application of MobileNets Convolutional Neural Network Model in Detecting Tomato Late Blight Disease," Tanzania Journal of Science: Vol. 48: Iss. 4, Article 17.
Available at:https://doi.org/10.4314/tjs.v48i4.17