Deep transfer learning for fine-grained maize leaf disease classification

Imran Khan

doi:10.1016/j.jafr.2024.101148

Deep transfer learning for fine-grained maize leaf disease classification

2024-06-01

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DOI：10.1016/j.jafr.2024.101148

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Machine learning (ML) can enhance agricultural yields by combating plant diseases and climate change. However, traditional image processing techniques for disease detection have limitations in robustness and generalizability. In this study, we investigate deep transfer learning for fine-grained disease classification in maize plants, which is a challenging task due to the subtle and nuanced disease patterns. We use four tailored deep learning frameworks: VGGNET, Inception V3, ResNet50, and InceptionResNetV2. ResNet50 achieves the highest validation accuracy of 87.51%, precision of 90.33%, and recall of 99.80%, demonstrating the efficacy and superiority of our approach. Our study offers an innovative solution for accurate disease classification in maize plants.

Keywords:

Deep transfer learning

Machine learning

Image processing techniques

Disease classification

Maize plants

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Deep transfer learning for fine-grained maize leaf disease classification

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