Crop, Fertilizer and Pesticide Recommendation using Ensemble Method and Sequential Convolutional Neural Network
Keywords:
Machine Learning, Recommendation System, K-nearest Neighbor(KNN), Support Vector Machine(SVM), Random Forest Algorithm, Sequential Convolutional Neural NetworkAbstract
Agriculture is the backbone of the Indian economy, with 60% - 70% of the Indian people relying on agriculture for subsistence. Unfortunately, farmers sometimes don't have the time needed to carefully consider all important facts before making decisions. As a result, they rely on agricultural experts, who may or may not always be available. With the aid of precision agriculture, these farmers are given knowledge on the specific crops that should be grown on their property. The major objective is to develop a website which makes it simple to use by using the Machine Learning model to generate the real-time prediction that analyzes environmental and soil factors like Nitrogen (N), Potassium (K), Phosphorous (P), pH, Temperature, Humidity, Soil moisture and Rainfall which suggests the best crop to grow using Ensemble Model through Majority Voting Mechanism, fertilizer to apply using Fertilizer Dictionary and pesticide based on the image analysis of the pest using Sequential Convolutional Neural Network(CNN) from Kaggle dataset. The resulting model when given inputs on the web interface recommends the crop suitable based on soil condition hence giving best decision on what crops to grow, what fertilizer to be used and helpful for identification of the pest and prescribe the appropriate dosage of pesticide.
Downloads
References
R. Gupta, A.K. Sharma, O. Garg, K. Modi, S. Kasim, Z. Baharum, H. Mahdin, and S.A. Mostafa, 2021. WB-CPI: Weather based crop prediction in India using big data analytics. IEEE access, 9, pp.137869-137885.
S.P. Raja, B. Sawicka, Z. Stamenkovic, and G. Mariammal, 2022. Crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers. IEEE Access, 10, pp.23625-23641.
M. Keerthana, K.J.M. Meghana, S. Pravallika, and M. Kavitha, 2021, February. An ensemble algorithm for crop yield prediction. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 963-970). IEEE.
S. Vaishnavi, M. Shobana, R. Sabitha, and S. Karthik, 2021, March. Agricultural crop recommendations based on productivity and season. In 2021 7th international conference on advanced computing and communication systems (ICACCS) (Vol. 1, pp. 883-886). IEEE.
R. Reshma, V. Sathiyavathi, T. Sindhu, K. Selvakumar, and L. SaiRamesh, 2020, October. IoT based classification techniques for soil content analysis and crop yield prediction. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 156-160). IEEE.
S.M. Pande, P.K. Ramesh, A. ANMOL, B.R. Aishwarya, K. ROHILLA, and K. SHAURYA, 2021, April. Crop recommender system using machine learning approach. In 2021 5th international conference on computing methodologies and communication (ICCMC) (pp. 1066-1071). IEEE.
V. Pandith, H. Kour, S. Singh, J. Manhas, and V. Sharma, 2020. Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis. Journal of scientific research, 64(2), pp.394-398.
Y.J.N. Kumar, V. Spandana, V.S. Vaishnavi, K. Neha, and V.G.R.R. Devi, 2020, June. Supervised machine learning approach for crop yield prediction in agriculture sector. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 736-741). IEEE.
P.S. Nishant, P.S. Venkat, B.L. Avinash, and B. Jabber, 2020, June. Crop yield prediction based on Indian agriculture using machine learning. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-4). IEEE.
M. Turkoglu, B. Yanikoğlu, and D. Hanbay, 2022. PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection. Signal, Image and Video Processing, 16(2), pp.301-309.
F. Jiang, Y. Lu, Y. Chen, D. Cai, and G. Li, 2020. Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture, 179, p.105824.
Y. Li, H. Wang, L.M. Dang, A. Sadeghi-Niaraki, and H. Moon, 2020. Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture, 169, p.105174.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.