Abstract
Plants, being one of the most important elements of the biosphere, are essentially useful for the survival of all living organisms. Plant seedlings are inevitable to produce cash crops at an adequate quantity since the world population is increasing. A crucial issue to be addressed in the production of good quality seedlings is the weed control. In order to create a feasible, effective and better approach for classifying seedlings, this article presents an AI-based approach that can accurately discriminate and categorize seedlings. In this approach, pretrained models are investigated to identify better deep model for efficient seedlings classification. A comparative analysis is also conducted to analyze the performance of deep models for plant seedlings classification. The findings demonstrate the efficiency of the Xception model in plant seedlings classification. The deep models used for comparison are InceptionResnetV2, Xception, InceptionV3, ResNet50 and MobileNetv2. All the employed models yielded accuracy rates above 90% of which the Xception model outperformed the other models by scoring an accuracy rate of 96%.
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Greeshma, R., Simon, P. (2024). Deep Learning Approach based Plant Seedlings Classification with Xception Model. In: Joby, P.P., Alencar, M.S., Falkowski-Gilski, P. (eds) IoT Based Control Networks and Intelligent Systems. ICICNIS 2023. Lecture Notes in Networks and Systems, vol 789. Springer, Singapore. https://doi.org/10.1007/978-981-99-6586-1_5
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DOI: https://doi.org/10.1007/978-981-99-6586-1_5
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