A highly accessible application for detection and classification of maize foliar diseases from leaf images - Khethisa, Joang Adolf (Department of Computer Science, 2017)
Crop diseases are a major impediment to food security in the developing world. The development of cheap and accurate crop diagnosis software would thus be of great benefit to the farming community. A number of previous studies, utilizing computer vision and machine-learning algorithms, have successfully developed applications that can diagnose crop diseases. However, these studies have primarily focussed either on developing large scale remote sensing applications more suited for large scale farming or on developing desktop/laptop applications and a few others on developing high end smartphone applications. Unfortunately, the attendant hardware requirements and expenses make them inaccessible to the majority of the subsistence farmers, especially those in sub-Saharan Africa where both smartphones and personal computers ownership is minimal. The primary objective of our research was to establish the feasibility of utilizing computer vision and machine learning techniques to develop a crop diseases diagnosis application that is not only accessible through personal computers and smartphones but is also accessible through any internet enabled feature phone. Leveraging methods established in previous papers, we successfully developed a prototype crop diseases diagnosis application capable of diagnosing two maize foliar diseases, Common Rust and Grey Leaf Spot. This application is accessible through personal computers and high end smartphones as well as through any internet enabled feature phones. The solution is a responsive web based application constructed using open source libraries whose diagnosing engine utilizes an SVM classifier that can be trained using either SIFT or SURF features. The solution was evaluated to establish classification accuracy, page load times when accessed from different networks and its cross-browser support. The system achieved 73.3% overall accuracy rate when tested using images identical to images end users would upload. Page load times were considerably long on GPRS and 2G network tests. However, they were comparable to average page load times users would experience when accessing google search engine pages from similar networks. Cross-browser support tests indicated that the system is fully compatible with all popular mobile and desktop browsers. Based on the evaluation results, we concluded that it is feasible to develop a crop diseases diagnosis application that in addition to being accessible through personal computers and smartphones can also be accessed through any internet enabled feature phones.