Machine Learning Adopted Multi-Class Classification of Plant Diseases with Sparse and Categorical IoT Data
Keywords:
Keywords: Plant disease identification, IoT sensor data, Predictive analytics, Decision analytics, Machine Learning, Data balancing, SMOTE algorithm.Abstract
manual diagnostic methods to sophisticated automated systems utilizing machine learning. Historically,farmers and experts relied on visual inspection, consultations, and laboratory tests to identify plantdiseases—a process that, while effective for small-scale applications, was often subjective, timeconsuming, and inconsistent, leading to delayed interventions and crop losses.
References
Tirkey D, Singh KK, Tripathi S. Performance analysis of AI-based solutions for crop disease identification detection, and classification. Smart Agric Technol. 2023. https://doi.org/10.1016/j.atech.2023.100238.
Ramanjot, et al. Plant disease detection and classification: a systematic literature review”. Sensors. 2023. https://doi.org/10.3390/s23104769.
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Published
2025-01-01
How to Cite
Dr. Ayesha Banu, Shivani Soumya Kandakatla, Gaje Harshitha, Bandi Deepak, Yara Anil Kumar. (2025). Machine Learning Adopted Multi-Class Classification of Plant Diseases with Sparse and Categorical IoT Data . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 7–21. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2268
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