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Supriya Kapase, Chaitanya Bari, Chaitrali Bhambure, Pallavi Chopade, Padmini Kondhalkar
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- Professor, Student, Student, Student, Student, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Pune, Pune, Pune, Pune, Pune, India, India, India, India, India
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Abstract
nIn recent years, there has been a growing interest and emphasis on agricultural land preparation and its implementation among researchers, primarily due to various factors. These factors include an increased focus within the research community, a rising demand for agricultural land, and the significance of assessing soil health for ensuring robust crop production. Picture request is one such philosophy for soil and land prosperity examination. It is a staggering measure having the effects of various parts. This paper has suggested an investigation into the stream, analyzing the problems it addresses, as well as its future possibilities. The emphasis is focused on the intelligent examination of various advanced and successful gathering frameworks and methodology. Here, taking into account the components has been tried these techniques have directed to work on the accuracy of the portrayal. Suitable use of the amount of features of remotely recognized data and picking the best sensible classifier are by and large huge for working on the accuracy of the gathering. The data- based game plan or Non-parametric classifier like brain network have procured pervasiveness for multisource data gathering lately. Not with remaining, there is at this point the degree of extra investigation, to decrease weaknesses in the improvement of accuracy of the Picture gathering instruments. By utilizing support vector machine calculation is utilized to suggest the harvests in light of the dirt. Within this project, we strongly advocate for the adoption of the KNN model by industries as well. We have created handcrafting dataset for industries. Around we are worked on 5 regions Konkan, Marathwada, Vidarbha, Nashik, Pune.
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Keywords: Convolutional Neural Network, Support Vector Machine, K Nearest Neighborhood, Crop Prediction System
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Full Text
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Browse Figures
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References
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1. Odysseas Vlachopoulos, Brigitte Leblon, Jinfei Wang, Ataollah Haddadi, Armand LaRocque and Greg Patterson “Evaluation of Crop Health Status With UAS Multispectral Imagery” IEEE Journal of Selected Topics in Applied Earth Observationsand Remote Sensing, Vol. 15, 2022.
2. Madhuri Shripathi Rao, Arushi Singh, N.V. Subba Reddy and Dinesh U Acharya “Crop prediction using machine learning” Journal of Physics: Conference Series AICECS 2021.
3. Seyed Mahdi Mirhoseini Nejad , Dariush Abbasi-Moghadam , Alireza Sharifi , Nizom Farmonov, Khilola Amankulova , and Mucsi Lászl ́z “Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 16, 2023.
4. Mrs. R. Usha Devi#1 , Dr. N.A. Sheela Selvakumari “Crop Prediction And MappingUsing Soil Features With Different Machine Learning Techniques” http://dx.doi.org/10.2139/ssrn.4097213.
5. Firdous Hina, Dr. Mohd. Tahseenul Hasan “Agriculture Crop Yield Prediction Using Machine Learning” International Journal for Research in Applied Science & Engineering Technology (IJRASET) Volume 10 Issue IV Apr 2022.
6. S.R. Rajeswari , Parth Khunteta, Subham Kumar,Amrit Raj Singh,Vaibhav Pandey “Smart Farming Prediction Using Machine Learning” International Journal of Innovative Technology and Exploring Engineering (IJITEE) Decision Analytics Journal Volume 3, June 2022, 100041.
7. John Carlo V. Puno, Rhen Anjerome R. Bedruz, Allysa Kate M. Brillantes “Soil Nutrient Detection using Genetic Algorithm” Manufacturing Engineering and Management Department 2022 IEEE.
8. R. Reshma, V. Sathiyavathi and T. Sindhu et al. “IoT based Classification Techniques for Soil Content Analysis and Crop Yield Prediction” Proceedings of the Fourth International Conference on I- SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Volume 3, vol.99, pp.247, 2020.
9. Sonal Jain, Dharavath Ramesh, “Machine Learning convergence for weather based crop selection” 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science.
10. G. Mariammal , A. Suruliandi , S. P. Raja , and E. Poongothai “Prediction of Land Suitability for Crop Cultivation Based on Soil and Environmental Characteristics Using Modified Recursive Feature Elimination Technique With Various Classifiers” IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2329-924X.
11. Mahendra N, Dhanush Vishwakarma, Nischitha K, Ashwini, Manjuraju M. R “Crop Prediction using Machine Learning Approaches” Volume 09, Issue 08 (August 2020).
12. Dharesh Vadalia, Minal Vaity, Krutika Tawate, Dynaneshwar Kapse, “Real Time soil fertility analyzer and crop prediction,” International Research Journal of Engineering and Technology, vol. 04, 2017.
13. Devi, M. P. K., Anthiyur, U., & Shenbagavadivu, M. S. (2016). Enhanced Crop Yield Prediction and Soil Data Analysis Using Data Mining. International Journal of Modern Computer Science, 4(6).
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International Journal of Algorithms Design and Analysis Review
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| Volume | 01 |
| Issue | 01 |
| Received | June 7, 2023 |
| Accepted | June 23, 2023 |
| Published | July 29, 2023 |
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