Application of Image Recognition Algorithms in the Detection of Philippine Lime Diseases

Calamansi has been declared as one of the most important fruit growing crops in the Philippines. However, due to certain bacteria, it is susceptible to certain diseases affecting its harvest rate. This paper aims to effectively monitor the state of the calamansi at its healthy state and at its diseased state. Specifically, it classifies diseases such as Citrus Canker, Citrus Scab, and Citrus Browning by utilizing existing image processing techniques for disease detection of different fruits and determining which algorithm is most apt for this application in terms of precision, accuracy and recall. Techniques such as K-Means Clustering, utilization of an Artificial Neural Network (ANN), feature extraction through GLCM along with the usage of a minimum distance classifier, a Support Vector Machine (SVM) classifier and other techniques and/or their combinations were explored and measured. The researchers performed two kinds of tests: 1×1 comparison and merged comparison. For the 1×1 comparison, making use of GrabCut, color feature extraction, and SVM produced the best overall results, with an overall average of 98% for precision, 95% for accuracy, 91% for recall, and 94% for F-score. Adaptive Gaussian Filtering along with texture feature extraction and SVM was the most accurate for detecting calamansi fruits with citrus canker and citrus scab. Overall, the two methods acquired the same average accuracy of 61%