Crop Yield Maximization Using an IoT-Based Smart Decision


  • Amita Shukla, Krishna Kant Agrawal


Fack news, RandomForest, J48, SMO, NaiveBayes, OE-MDL, Ibk LSTM


This paper conducted a comprehensive analysis of the integration between sensor technologies and machine learning algorithms in terms of crop yield prediction for precision agriculture. Appreciating the role of precision yield estimate in mitigating global food challenges, this paper discusses various sensor technologies including NPK sensors among others; their strengths and weaknesses are highlighted. An in-depth analysis of machine learning algorithms such as Decision Trees, Naïve Bayes, Support Vector Machines, K-Nearest Neighbors and Ensemble Learning reveal their comparative performances with regards to adopting them into agricultural practices. In addition, the use of Multiple Linear Regression for planning rainfall enables an interdisciplinary approach to precision agriculture based on both soil characteristics and climatic conditions. The discussion covers the emerging trends, patterns and gaps in previous research evidence on this topic along with possible implications for future studies or concrete implementation. Through identifying the challenges and limitations, including periodic sensor calibration as well as algorithm interpretability furthered by the review our complex reality of precision farming.


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How to Cite

Krishna Kant Agrawal, A. S. . (2024). Crop Yield Maximization Using an IoT-Based Smart Decision. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 163–168. Retrieved from



Research Article