AgriSense: Harnessing Machine Learning for Intelligent Crop Recommendation and Sustainable Agriculture
Keywords:
Agriculture, Crop Recommendation, Ensemble Learning, K-fold, ML AlgorithmsAbstract
Agriculture plays a crucial role in economic development, especially in countries such as Bangladesh and India where a large proportion of the population depends on farming for their livelihood, and with increasing climate variability and resource constraints, intelligent data driven approaches are essential to improve crop productivity, sustainability, and efficient resource utilization; this study presents AgriSense, a machine learning based framework for intelligent crop recommendation that assists farmers in selecting suitable crops by analyzing rainfall patterns, climatic conditions, and soil fertility, addressing the limitations of traditional farming practices that often lack precision and lead to inefficient resource use and suboptimal crop choices; the dataset was constructed by integrating multiple publicly available datasets related to rainfall, climate, soil nutrients, and fertilizer usage specific to India, providing a rich and diverse foundation for model training and evaluation; four machine learning algorithms, Random Forest, Logistic Regression, Support Vector Machine, and Decision Tree, were applied for crop prediction, with K fold cross validation used to ensure model reliability and generalization, and performance evaluated using confusion matrices and ROC curves, the experimental results demonstrate that the Random Forest model achieved the best performance with 99.59% accuracy, 99.62% precision, 99.59% recall, a 99.59% F1 score, and 100% AUC, highlighting the effectiveness of AgriSense as a practical, scalable, and data driven solution for supporting sustainable agricultural decision making and transforming traditional farming into a more resource efficient and intelligent process.
References
Y. Akkem, S. K. Biswas, and A. Varanasi, “Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming,” Neural Computing and Applications, vol. 36, no. 32, pp. 20011–20025, Nov. 2024.
S. M. Pande et al., “Crop recommender system using machine learning approach,” in Proc. 5th International Conference of Computing Methodologies and Communication (ICCMC), 2021, pp. 1066–1071.
M. Hasan, “Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation,” Frontiers in Plant Science, vol. 14, Aug. 2023, Art. no. 1234555.
B. Dey, J. Ferdous, and R. Ahmed, “Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables,” Heliyon, vol. 10, no. 3, Feb. 2024.
N. Agrawal, H. Govil, and T. Kumar, “Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem,” Environment, Development and Sustainability, vol. 27, no. 6, pp. 13689–13726, Jun. 2025.
A. Palakshappa et al., “Smart irrigation with crop recommendation using machine learning approach,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 3, pp. 1952–1960, Jun. 2024.
A. Ingle, “Crop Recommendation Dataset,” Kaggle, 2020.
P. S. Gopi and M. Karthikeyan, “Multimodal machine learning based crop recommendation and yield prediction model,” Intelligent Automation & Soft Computing, vol. 36, no. 1, Apr. 2023.
A. Joshi, B. Pradhan, “Remote-sensing data and deep-learning techniques in crop mapping and yield prediction: A systematic review,” Remote Sensing, vol. 15, no. 8, p. 2014, Apr. 2023.
M. Kuradusenge, E. Hitimana, “Crop yield prediction using machine learning models: Case of Irish potato and maize,” Agriculture, vol. 13, no. 1, p. 225, Jan. 2023.
S. Kiruthika and D. Karthika, “IoT-based professional crop recommendation system using a weight-based long-term memory approach,” Measurement: Sensors, vol. 27, p. 100722, Jun. 2023.
P. Sharma, P. Dadheech,“Predicting agriculture yields based on machine learning using regression and deep learning,” IEEE Access, vol. 11, pp. 111255–111264, Oct. 2023.
M. Shripathi Rao, A. Singh, N. V. Subba Reddy, and D. U. Acharya, “Crop prediction using machine learning,” Journal of Physics: Conference Series, vol. 2161, no. 1, p. 012033, Jan. 2022.
T. Padma and D. Sinha, “Crop yield prediction using improved random forest,” ITM Web of Conferences, vol. 56, p. 02007, 2023.
D. Elavarasan and P. D. Vincent, “A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 11, pp. 10009–10022, Nov. 2021.
