Supriya Kapase
Chaitanya Bari
Chaitrali Bhambure
Pallavi Chopade
Padmini Kondhalkar
- Professor Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus Pune India
- Student Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus Pune India
- Student Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus Pune India
- Student Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus Pune India
- Student Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus Pune India
Abstract
In 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 dimensions. 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, various components have been taking into account and these techniques have been directed to work on the accuracy of the portrayal. Suitable use of the features of remotely recognized data and picking the best sensible classifier are by and large huge for working on the accuracy of the data collected. The data-based game plan or non-parametric classifier like brain network has procured pervasiveness for multisource data gathering lately. Nevertheless, there is at this point the degree of extra investigation, to decrease weaknesses in the improvement of accuracy of the image gathering instruments. Support vector machine calculation is utilized to suggest the harvests in light of the soil conditions. Within this project, we strongly advocate for the adoption of the K nearest neighbor model by industries as well. We have created a skilled dataset for industries. We have worked on five regions, namely Konkan, Marathwada, Vidarbha, Nashik, and Pune.
Keywords: Convolutional neural network, support vector machine, K nearest neighborhood, crop prediction system
References
- Vlachopoulos O, Leblon B, Wang J, Haddadi A, LaRocque A, Patterson G. Evaluation of crop health status with UAS multispectral imagery. IEEE J Select Topics Appl Earth Observ Remote Sensing. 2022; 15: 297–308.
- Rao MS, Singh A, Subba Reddy NV, Acharya DU. Crop prediction using machine learning. J Phys Conf Series. 2021; 2161: 012033. doi: 10.1088/1742-6596/2161/1/012033.
- Nejad SMM, Abbasi-Moghadam D, Sharifi A, Farmonov N, Amankulova K, Lászlź M. Multispectral crop yield prediction using 3D-convolutional neural networks and attention convolutional LSTM approaches. IEEE J Select Topics Appl Earth Observ Remote Sensing. 2023; 16: 254–266. doi: 10.1109/JSTARS.2022.3223423.
- Usha Devi R, Sheela Selvakumari NA. Crop prediction and mapping using soil features with different machine learning techniques. In: Proceedings of the International Conference on Innovative Computing and Communication (ICICC) 2022, February 19–120, New Delhi, India, 2022. doi: 10.2139/ssrn.4097213.
- Hina F, Hasan MT. Agriculture crop yield prediction using machine learning. Int J Res Appl Sci Eng Technol. 2022; 10 (IV): 910–915.
- Rajeswari SR, Khunteta P, Kumar S, Singh AR, Pandey V. Smart farming prediction using machine learning. Int J Innov Technol Exploring Eng. 2019; 8 (7): 190–194.
- Puno JCV, Bedruz RAR, Brillantes AKM, Vicerra RRP, Bandala AA, Dadios EP. Soil nutrient detection using genetic algorithm. In: 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Laoag, Philippines, November 29–December 1, 2019. pp. 1–6. doi: 10.1109/HNICEM48295.2019.9072689.
- Reshma R, Sathiyavathi V, Sindhu T, Selvakumar K, SaiRamesh L. IoT based classification techniques for soil content analysis and crop yield prediction. In: Proceedings of the 2020 Fourth International Conference on I- SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), October 7–9, 2020. Palladam, India. pp. 156–160.
- Jain S, Ramesh D. Machine learning convergence for weather based crop selection. In: 2020 IEEE International Students’ Conference on Electrical,Electronics and Computer Science (SCEECS), Bhopal, India, February 22–23, 2020. pp. 1–6. doi: 10.1109/SCEECS48394.2020.75.
- Mariammal G, Suruliandi A, Raja SP, Poongothai E. Prediction of land suitability for crop cultivation based on soil and environmental characteristics using modified recursive feature elimination technique with various classifiers. IEEE Transac Comput Soc Syst. 2021; 8 (5): 1132–1142. doi: 10.1109/TCSS.2021.3074534.
- Mahendra N, Vishwakarma D, Nischitha K, Ashwini, Manjuraju MR. Crop prediction using machine learning approaches. Int J Eng Res Technol. 2020; 9 (8): 23–26.
- Vadalia D, Vaity M, Tawate K, Kapse D. Real time soil fertility analyzer and crop prediction. Int Res J Eng Technol. 2017; 4 ()3: 1497–1499.
- Devi MPK, Anthiyur U, Shenbagavadivu MS. Enhanced crop yield prediction and soil data analysis using data mining. Int J Modern Computer Sci. 2016; 4(6).
| Volume | |
| Received | June 7, 2023 |
| Accepted | June 23, 2023 |
| Published | August 24, 2023 |