Detection and Classification of Temporal Changes for Citrus Canker Growth Rate Using Deep Learning

Citrus canker is among the major plant diseases caused by Xanthomonas citri which affects the quality and quantity of citrus fruit. This results in the reduction of citrus production which causes a huge financial loss and livelihood of the farming community. Thus, it is critically important to build a robust, accurate, and time-efficient detection method for real-time identification of the disease. Due to their powerful learning capabilities and improved feature extraction, deep learning approaches have made it feasible to carry out a number of tasks related to the identification of citrus canker in citrus leaves. Previous research has primarily focused on detecting citrus canker on fruits, early detection on leaves can facilitate the adoption of preventive measures before the disease reaches a critical stage. This paper proposes a novel deep learning-based approach for determining the growth rate of citrus canker by classifying it into six distinct stages: water soaking, yellow chlorosis/initiation, chlorosis, blister formation, canker development start, canker infection (50% of the inoculated area), and canker infection (100% of the inoculated area). The proposed approach involves image conversion, size reduction, image augmentation, and the utilization of DenseNet-121. Experimental results demonstrate a classification accuracy of 98.97% using the suggested approach. The Accuracy was 98.97% with macro precision 97%, weighted precision 99%, Macro recall 98%, weighted recall 98%, macro F1_Score 97% and weighted F1_Score 98%. This study presents a unique technique for detecting and classifying the growth rate of citrus canker based on six different stages, while also calculating the temporal change in the affected area of the disease in inoculated citrus leaves. Furthermore, a mathematical model is proposed to predict the disease’s growth rate at any given time, offering valuable insights for disease management and prevention.