A Hybrid Machine Learning Model to Recognize and Detect Plant Diseases in Early Stages

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Sudeepthi Govathoti
Deepthi Kamidi
Y. Madhavi Reddy
Mahesh Kotha
Gangolu Yedukondalu
Y. Krishna Bhargavi
Bh. Prashanthi


This paper presents an improved Inception module to recognise and detect plant illnesses substituting the original convolutions with architecture based on modified-Xception (m-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach is capable of achieving the specified level of performance, with an average recognition fineness of 99.73 on the public dataset and 98.05 on the domestic dataset, respectively.

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How to Cite
Govathoti, S. ., Kamidi, D. ., Reddy, Y. M. ., Kotha, M. ., Yedukondalu, G. ., Bhargavi, Y. K. ., & Prashanthi, B. . (2023). A Hybrid Machine Learning Model to Recognize and Detect Plant Diseases in Early Stages. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 04–15. https://doi.org/10.17762/ijritcc.v11i9s.7390


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