Multi-Spectral Image Analysis Using Different CNN Models to Detect the Plant Diseases in its Early Stages

The Researchers and academicians are continuously working on minimizing the production losses due to various plant diseases. Therefore, recent technologies such as artificial intelligence (AI), and machine learning (ML) are playing a crucial role in detecting plant diseases in their early stages. These technologies help classifying plant leaves into ‘healthy’ and ‘rusty’ or ‘diseased’ leaves. It is difficult for human being to detect the plant diseases and take remedial action within stipulated time period. Hence, this research work addresses comparison of different convolutional neural network (CNN) models like Alexnet, Resent18, Resnet50, Xception, VGG16, VGG19, InceptionV3, InceptionResentV2 etc. and concluded with the top CNN models with good filters used to capture the plant leaves images. Proposed research work uses different filters dataset like K590, K665, K720, K850, BlueIR, Hotmirror. Plant disease detection requires accurately detecting the rust, or disease on the leaves immediately and efficiently. CNN models help classifying the plant leaves with higher accuracy and precision. Proposed research work gave accuracies for different filters with different models and found that, for K850 filter accuracy is 72.72% using balance efficientnetB0 CNN model, for K720 filter accuracy is 81.81%using balance efficientnet B0 CNN model, for K665 filter accuracy is 84.09% using balance efficientnetB0 CNN model, for K590 filter accuracy is 90.90% using balance MobilenetV2 CNN model, and for the hotmirror filter accuracy is 93.18% using balance Xception & for the blueIR filter accuracy is 81.81% using balance Xception CNN model.

@article{Bhise2025,
  title     = {Multi-Spectral Image Analysis Using Different CNN Models to Detect the Plant Diseases in its Early Stages},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Dhiraj Bhise and Sunil Kumar and Hitesh Mohapatra},
  doi       = {10.14569/IJACSA.2025.01612119},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612119}
}