SMART AGRICULTURE CROP SUGGESTION USING SATELLITE DATA WITH ML

“Smart Agriculture Crop Suggestion Using Satellite Data with Machine Learning” presents an intelligent and data-driven approach to modern agricultural decision-making. Traditional farming practices, which primarily rely on historical experience and generalized seasonal patterns, often struggle to cope with climate variability and soil degradation. To address these challenges, the proposed system integrates satellite-based environmental monitoring with advanced Machine Learning algorithms to provide accurate and location-specific crop recommendations. By utilizing satellite data from organizations such as the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), the system gathers large-scale environmental parameters including land surface temperature, soil moisture, rainfall patterns, and vegetation indices. These are combined with ground-level inputs such as soil pH, nutrient content (NPK), humidity, and temperature to create a comprehensive agricultural dataset.

1.                           M. A. H. Chowdhury, M. A. Hossain, and H. T. S. Jesmin, “Crop Yield Prediction Using Machine Learning Based on Weather and Satellite Data,” in 2020 International Conference on Computational Performance Evaluation (ComPE), Dhaka, Bangladesh, 2020, pp. 1–6. 2.                           S. Kamilaris and F. X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, May 2018. 3.                           A. Maqsood, A. Hussain, A. Riaz, and G. Abbas, “Crop Recommendation System Using Machine Learning,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 6, pp. 412–416, Jun. 2019. 4.                           L. Monteiro, A. C. Rodrigues, and V. Z. R. Tannús, “Satellite Image Processing for Precision Agriculture: A Review,” Journal of Agricultural Informatics, vol. 8, no. 4, pp. 19–32, Dec. 2017. 5.                           Ramesh and V. M. Varaprasad, “Machine Learning Techniques for Crop Yield Prediction: A Review,” International Journal of Computer Applications, vol. 182, no. 45, pp. 15–23, Jul. 2019.   6.                           S. P. Singh and A. K. Panigrahi, “Decision Tree and Random Forest Algorithms for Crop Recommendation,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 3, pp. 85–89, Mar. 2020. 7.                           F. Shi and L. Cai, “A Hybrid Model Based on Satellite Image Data and Machine Learning Techniques for Crop Yield Estimation,” Remote Sensing, vol. 12, no. 9, p. 1468, May 2020. 8.                           R. S. S. Kaul and P. Sharma, “Soil and Weather DataBased Crop Prediction System Using Data Mining Approach,” International Journal of Computer Applications, vol. 174, no. 1, pp. 25–30, Aug. 2017. 9.                           P. H. R. Sahu, “Role of Soil pH and Nutrient Availability in Crop Production,” Journal of Soil Science and Environmental Management, vol. 10, no. 2, pp. 14–21, Feb. 2019. 10.                         J.             Zhang,   Y.           Wei,       X.           Li,          and         H.           Shen,                     “CropClassification and Yield Prediction Using Remote Sensing and Machine Learning Approaches: A Review,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 1–19, 2020