1st International Conference on Emerging Trends in Mechanical Sciences for Sustainable Technologies
Agri Crop Recommendation System Using Suitable Machine Learning Techniques
Ananthi S, Bauvya Shree T G, Bhava Tharani R, Geethanjali R, and Suchithra B, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India.
Sathya R, Kongu Nadu College of Engineering and Technology, Trichy, Tamil Nādu, India.
In coastal states like Tamil Nadu, agricultural uncertainty lowers productivity due to a lack of modern technology, unstable climate changes, and a lack of good irrigation facilities. The ability to utilize modern technology in agriculture is mandatory in the current situation. Agricultural issues like crop rotation, crop forecasting, crop protection, water requirement, and fertilizer requirements can be solved using machine learning technology. It is possible to forecast and derive a precise model from the data using machine learning techniques. Agricultural crop recommendations based on productivity and season is proposed in this work. This might make it possible for prospective farmers to engage in improved agriculture. A farmer may receive a system of ideas to increase production and can move forward smoothly. Crops are advised based on quantity and weather concerns. The analysis of the crop dataset resulted in the recommendation of crops based on their productivity and growing season.
Keywords
K-Nearest Neighbors, Machine Learning, Random Forest, Support Vector Machine, Naive Bayes, Recommender System.
Neerkathalingam.V; "Agricultural crop recommendation based on productivity and season", International Research Journal of Modernization in Engineering Technology and Science, Volume:04/ Issue:05/May-2022.
R.Parvathi, "Improvement of Crop Production Using Recommender System by Weather Forecasts," International Conference on recent Trends in Advanced Computing by Weather Forecasts, 2019, ICRTAC-2019.
Vanitha. D "Productivity and Season Based Crop Yield Prediction Using Machine Learning Algorithm," International Journal of Research Publications and Reviews, Vol 3, no8, pp 204-207, August 2022
S. Agarwal and S. Tarar, “A Hybrid Approach for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms,” Journal of Physics: Conference Series, vol. 1714, no. 1, p. 012012, Jan. 2021, doi: 10.1088/1742-6596/1714/1/012012.
K. Moraye et al., “Crop Yield Prediction Using Random Forest Algorithm for Major Cities in Maharashtra State,” International Journal of Innovative Research in Computer Science & Technology, vol. 9, no. 2, pp. 40–44, Mar. 2021, doi: 10.21276/ijircst.2021.9.2.7.
S. Sampathkumar and R. Rajeswari, “An Automated Crop and Plant Disease Identification Scheme Using Cognitive Fuzzy C-Means Algorithm,” IETE Journal of Research, vol. 68, no. 5, pp. 3786–3797, Jun. 2020, doi: 10.1080/03772063.2020.1780163.
S. Ramasamy, A. Selvarajan, V. Kaliyaperumal, and P. Aruchamy, “A hybrid location‐dependent ultra convolutional neural network‐based vehicle number plate recognition approach for intelligent transportation systems,” Concurrency and Computation: Practice and Experience, vol. 35, no. 8, Jan. 2023, doi: 10.1002/cpe.7615.
Manjunathan.,"Crop Yield Prediction Using Linear Support Vector Machine," European Journal of Molecular and Clinical Medicine Volume 7, Issue 06,2020.
R. Sathya, S. Ananthi, R. Rupika, N. Santhiya, and K. Lavanya, “Average Intensity Sign (AIS) Feature based Offline Signature Verification for Forgery Detection using Machine Learning,” 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Nov. 2022, doi: 10.1109/icaiss55157.2022.10010812.
W. Li et al, “Crop Recommendation System Based on Convolutional Neural Networks and Transfer Learning", International Conference on Industrial Technology and Management (2021).
S. Ahmed et al.,” Agricultural Crop Recommendation Using Artificial Bee Colony Algorithm”, International Conference on Machine Learning and Data Engineering (2020).
S. Singh and R. Kumar "Crop Recommendation System for Rice Cultivation in India Using Decision Trees,". Conference: International Conference on Sustainable Computing and Communication Systems (2020).
D. Zhou et al. "Crop Recommendation Based on Multispectral Imagery and Machine Learning Techniques," Conference: International Conference on Information Technology and Multimedia (2021).
M. D. Dibie et al: "An Intelligent Crop Recommendation System for Smallholder Farmers in Africa," Conference: IEEE International Conference on Industrial Engineering and Engineering Management (2021).
J. Kim et al. "Crop Recommendation System Based on Clustering and Decision Trees,” Conference: International Conference on Advanced Communication Technologies and Networking (2021).
M. A. Ali and S. U. Amin. "Agricultural Crop Recommendation Using Ensemble Learning Techniques," Conference: International Conference on Information Science and Systems (2020).
Y. Li, C. Chen, F. Liu, and J. Wang, “Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning,” Journal of Food Quality, vol. 2022, pp. 1–11, Mar. 2022, doi: 10.1155/2022/9297205.
Cite this article
Ananthi S, Bauvya Shree T G, Bhava Tharani R, Geethanjali R, Suchithra B and Sathya R, “Agri Crop Recommendation System Using Suitable Machine Learning Techniques”, Advances in Intelligent Systems and Technologies, pp. 101-109, August. 2023. doi:10.53759/aist/978-9914-9946-4-3_16
