Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning
Eric Hitimana,
Martin Kuradusenge,
Omar Janvier Sinayobye,
Chrysostome Ufitinema,
Jane Mukamugema,
Theoneste Murangira,
Emmanuel Masabo,
Peter Rwibasira,
Diane Aimee Ingabire,
Simplice Niyonzima,
Gaurav Bajpai,
Simon Martin Mvuyekure,
Jackson Ngabonziza
Affiliations
Eric Hitimana
Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Martin Kuradusenge
Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Omar Janvier Sinayobye
Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Chrysostome Ufitinema
Department of Biology, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Jane Mukamugema
Department of Biology, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Theoneste Murangira
Department of Computer Science, University of Rwanda, Kigali P.O. Box 2285, Rwanda
Emmanuel Masabo
Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Peter Rwibasira
Department of Biology, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Diane Aimee Ingabire
Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Simplice Niyonzima
Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Gaurav Bajpai
Directorate of Grants and Partnerships, Kampala International University, Kansanga, Kampala P.O. Box 20000, Uganda
Simon Martin Mvuyekure
Crop Innovation and Technology Transfer, Traditional Export Crops Programme, Rwanda Agriculture Board, Kigali P.O. Box 5016, Rwanda
Coffee leaf diseases are a significant challenge for coffee cultivation. They can reduce yields, impact bean quality, and necessitate costly disease management efforts. Manual monitoring is labor-intensive and time-consuming. This research introduces a pioneering mobile application equipped with global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf disease detection. The application integrates advanced deep learning (DL) techniques to empower farmers and agronomists with a rapid and accurate tool for identifying and managing coffee plant health. Leveraging the ubiquity of mobile devices, the app enables users to capture high-resolution images of coffee leaves directly in the field. These images are then processed in real-time using a pre-trained DL model optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3, VGG16, and DenseNet, were experimented with on the dataset. All models showed promising performance; however, DenseNet proved to have high scores on all four-leaf classes with a training accuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each captured image, providing valuable location-specific information. Through extensive experimentation and validation, the app demonstrates impressive accuracy rates in disease classification. The results indicate the potential of this technology to revolutionize coffee farming practices, leading to improved crop yield and overall plant health.
