SMART AGRICULTURE SYSTEM FOR CROP SELECTION AND DISEASE DETECTION USING AI
Agriculture plays a vital role in ensuring food security and supporting economic development. However, the sector is increasingly challenged by factors such as climate change, unpredictable weather patterns, soil degradation, and the rapid spread of crop diseases. These challenges often lead to reduced crop yield and financial losses for farmers. To overcome these limitations, the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) has become essential in modern agriculture. This paper presents a Smart Agriculture System designed for efficient crop selection and early disease detection using Al techniques. The proposed system utilizes various datasets, including soil properties, weather conditions, and environmental parameters, to recommend the most suitable crops for a given region. Machine learning algorithms are employed to analyze these factors and provide accurate predictions that support better agricultural planning.
K, F., C, E., K, M. & M.Karthika, (2026). Smart Agriculture System for Crop Selection and Disease Detection Using AI. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.200
K, Femina, et al.. "Smart Agriculture System for Crop Selection and Disease Detection Using AI." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.200.
K, Femina,Elakkiya C,Meenatchi K, and M.Karthika. "Smart Agriculture System for Crop Selection and Disease Detection Using AI." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.200.
2.V. Nikitha, N. K. S. Keerthan, M. S. Praneeth, and T. Amrita, "Leaf Disease Detection and Classification," Procedia Computer Science, vol. 218, pp. 291–300,2023.
3.S. Harakannanavar, J. M. Rudagi, V. I. Puranikmath, A. Siddiqua, and R. Pramodhini, "Plant Leaf Disease Detection Using Computer Vision and Machine Learning Algorithms," Global Transitions Proceedings, vol. 3, pp. 305–310, 2022.
4.A. Pandian, K. Kanchanadevi, V. D. Kumar, E. Jasinska, R. Gono, Z. Leonowicz, and M. Jasinski, "A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection," Electronics, vol. 11, no. 8, p. 1266, Apr.2022.
5.P. Praveen, R. Nakka, A. Chokka, V. N. Thatha, S. S. Vellela, and U. Sirisha, "A Novel Classification Approach for Grape Leaf Disease Detection Based on Different Attention Deep Learning Techniques," International Journal of Advanced Computer Science and Applications, vol. 14, no. 6, pp. 1199–1209, 2023.
6.Chen, B. Han, X. Wang, J. Zhao, W. Yang, and Z. Yang, "Machine Learning Methods in Weather and Climate Applications: A Survey," Preprints.org. 2023. doi: 10.20944/preprints202309.1764.v2.)
7.Tarek, H. Aly, S. Eisa, and M. Abul-Soud, "Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment," Electronics, vol. 11, no. 1, p. 140, Jan. 2022.