Publications

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 laborintensiveand time-consuming. This research introduces a pioneering mobile application equippedwith global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf diseasedetection. The application integrates advanced deep learning (DL) techniques to empower farmersand 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 ofcoffee leaves directly in the field. These images are then processed in real-time using a pre-trained DLmodel optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3,VGG16, and DenseNet, were experimented with on the dataset. All models showed promisingperformance; however, DenseNet proved to have high scores on all four-leaf classes with a trainingaccuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each capturedimage, providing valuable location-specific information. Through extensive experimentation andvalidation, the app demonstrates impressive accuracy rates in disease classification. The resultsindicate the potential of this technology to revolutionize coffee farming practices, leading to improvedcrop yield and overall plant health.