NOVEL PERFORMANCE ANALYSIS OF YOLOV5 AND YOLOV8 FOR CURCUMA LONGA LEAF DISEASE IDENTIFICATION

The medical plant leaf disease detection plays a crucial role in the production of high-quality medicines globally. Particularly, the medicinal leaves used in Ayurveda medicine have significantly reduced the impact of COVID-19 in India and other parts of the world. Among such medicinal plants, the Curcuma longa, popularly known as turmeric, acts as an antibiotic that reduces the impact of lung-infected diseases. The current study concentrates on identifying early diseases in Curcuma longa leaves to enhance production, a significant challenge for farmers and practitioners. The current study pinpoints the most effective deep learning algorithms for distinguishing between the three classes of Curcuma longa: healthy leaves, leaf blotch, and leaf spot. The study employed the most popular and successful deep learning methods, such as Yolo V5 and V8, to identify the diseases for the first time on the 'Duggirala variant Curcuma longa Dataset', and achieved very high classification results. The mean average precision (mAP) results of YoloV5 have achieved 98.6%, whereas the YoloV8 method attained only 86.1%. We base the performance metrics of the two algorithms on training loss and validation loss, characterizing both training and validation losses with parameters like objectness (85%), box validation (96%), and classification analysis (98.2%). Based on our experimentation, we found that YoloV8 exhibits very high training loss and validation loss, while YoloV5 shows minimal losses. The experimentation results state that YoloV5 is best suited to detect the diseased and healthy classes for the novel Duggirala variant Curcuma longa Dataset. Overall, Yolov5 outperforms YoloV8 by 12.8 percent, and we recommend using the YoloV5 model for smart farming in turmeric plantations over other deep-learning models.