| dc.description.abstract | Agriculture is crucial for economic growth, with automation emerging as a global concern due to the increasing
population and food demand. Beyond simple cultivation, agriculture is vital to communities, the foundation of
economies, and the first line of defence against the threat of food insecurity. Plant diseases pose a constant threat
to this vital industry, while they also present several other difficulties. Plant diseases also pose a threat to crop
productivity and the fragile balance that exists between food production and the growing needs of an expanding
world population. Beans are a vital crop that has served mankind as a nutritious crop throughout history. This
research work has classified bean leaf diseases of angular leaf spot, bean rust, and healthy bean leaves using
Artificial Intelligence fused technologies. While testing on preprocessing methods, image-enhancing algorithms
such as Fast super resolution Convolutional Neural Networks (FSRCNN), Efficient Sub-Pixel Convolutional
Neural Networks (ESPCN), and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) were
used to boost image quality in the respective training phases. Out of the pre-trained Convolutional Neural
Networks (CNN) such as MobileNet, MobileNetV2, EfficientNetB0 - B6 models, NasNet, and You Only Look Once
- Version 8 (YOLOv8) model, the YOLOv8 model gave an impressive model accuracy of 99.6% when the images
were enhanced using ESPCN algorithm while classifying the images accurately. YOLOv8 proved the best model
performance by scoring optimal metric values compared to CNN models. Therefore, it can be concluded that
YOLOv8 with ESPCN can be used to efficiently classify bean leaf diseases and achieve promising results. | en_US |
