Random forest algorithm use for crop recommendation
Abstract
The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%.
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References
Shilpa Mangesh Pande, Dr. Prem Kumar Ramesh, Anmol, B.R Aishwarya, karuna Rohilla, Kumar Shaurya. “Crop recommender system using machine learning approach”, Proceedings of the Fifth International Conference on Computing Methodologies and Communication (ICCMC 2021) IEEE Aydin, O.F., Gokasar, I. and Kalan, O., 2020. Matching algorithm for improving ride-sharing by incorporating route splits and social factors. PloS one, 15(3), p.e0229674.
Umamaheswari S, Sreeram S, Kritika N, Prasanth DJ, “BIoT: Blockchain-based IoT for Agriculture”, 11th International Conference on Advanced Computing (ICoAC), 2019 Dec 18 (pp. 324-327). IEEE.
Jain A. “Analysis of growth and instability in the area, production, yield, and price of rice in India”, Journal of Social Change and Development, 2018.
Paithane, Pradip Mukundrao. "Yoga Posture Detection Using Machine Learning." Artificial Intelligence in Information and Communication Technologies, Healthcare and Education: A Roadmap Ahead (2022): 27.
Sagar BM, Cauvery NK., “Agriculture Data Analytics in Crop Yield Estimation: A Critical Review”, Indonesian Journal of Electrical Engineering and Computer Science, 2018 Dec;12(3):1087-93.
Johnson LK, Bloom JD, Dunning RD, Gunter CC, Boyette MD, Creamer NG, “Farmer harvest decisions and vegetable loss in primary production. Agricultural Systems”, 2019 Nov 1;176:102672.
Sriram Rakshith.K, Dr.Deepak.G, Rajesh M, Sudharshan K S, Vasanth ,S Harish Kumar N, “A Survey on Crop Prediction using Machine Learning Approach”, In International Journal for Research in Applied Science & Engineering Technology (IJRASET), April 2019, pp( 3231-3234).
S. Pavani, Augusta Sophy Beulet P., “Heuristic Prediction of Crop Yield Using Machine Learning Technique”, International Journal of Engineering and Advanced Technology (IJEAT), December 2019, pp(135-138).
Nigam, Aruvansh, Saksham Garg, Archit Agrawal, and Parul Agrawal. "Crop yield prediction using machine learning algorithms." In 2019 Fifth International Conference on Image Information Processing (ICIIP), pp. 125-130. IEEE, 2019.
Paithane, Pradip Mukundrao, and S. N. Kakarwal. "Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach." International Journal of Intelligent Systems and Applications in Engineering 10.1 (2022): 98-104.
Paithane, Pradip M., S. N. Kakarwal, and D. V. Kurmude. "Automatic Seeded Region Growing with Level Set Technique Used for Segmentation of Pancreas." Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) 12. Springer International Publishing, 2021.
Wagh, Sarita Jibhau, Pradip M. Paithane, and S. N. Patil. "Applications of Fuzzy Logic in Assessment of Groundwater Quality Index from Jafrabad Taluka of Marathawada Region of Maharashtra State: A GIS Based Approach." International Conference on Hybrid Intelligent Systems. Cham: Springer International Publishing, 2021.
Kakarwal, Sangeeta, and Pradip Paithane. "Automatic pancreas segmentation using ResNet-18 deep learning approach." System research and information technologies 2 (2022): 104-116.
Paithane, Pradip, Sarita Jibhau Wagh, and Sangeeta Kakarwal. "Optimization of route distance using k-NN algorithm for on-demand food delivery." System research and information technologies 1 (2023): 85-101.
Paithane, Pradip M., and S. N. Kakarwal. "Automatic determination number of cluster for multi kernel NMKFCM algorithm on image segmentation." Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) held in Vellore, India, December 6-8, 2018, Volume 2. Springer International Publishing, 2020.
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