Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review
Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of wa...
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| Format: | Article |
| Language: | English |
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Technoscience Publications
2024-12-01
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| Series: | Nature Environment and Pollution Technology |
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| Online Access: | https://neptjournal.com/upload-images/(14)B-4166.pdf |
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| author | V. Ramesh and P. Kumaresan |
| author_facet | V. Ramesh and P. Kumaresan |
| author_sort | V. Ramesh and P. Kumaresan |
| collection | DOAJ |
| description | Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models. |
| format | Article |
| id | doaj-art-8cf26b37e8cc474e94680b948dea8a03 |
| institution | Kabale University |
| issn | 0972-6268 2395-3454 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Technoscience Publications |
| record_format | Article |
| series | Nature Environment and Pollution Technology |
| spelling | doaj-art-8cf26b37e8cc474e94680b948dea8a032025-01-20T07:13:36ZengTechnoscience PublicationsNature Environment and Pollution Technology0972-62682395-34542024-12-012342071208610.46488/NEPT.2024.v23i04.014Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive ReviewV. Ramesh and P. KumaresanAgriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.https://neptjournal.com/upload-images/(14)B-4166.pdfcrop yield prediction, machine learning, deep learning, artificial neural networks, optimization algorithms |
| spellingShingle | V. Ramesh and P. Kumaresan Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review Nature Environment and Pollution Technology crop yield prediction, machine learning, deep learning, artificial neural networks, optimization algorithms |
| title | Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review |
| title_full | Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review |
| title_fullStr | Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review |
| title_full_unstemmed | Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review |
| title_short | Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review |
| title_sort | advancements in machine learning and deep learning techniques for crop yield prediction a comprehensive review |
| topic | crop yield prediction, machine learning, deep learning, artificial neural networks, optimization algorithms |
| url | https://neptjournal.com/upload-images/(14)B-4166.pdf |
| work_keys_str_mv | AT vrameshandpkumaresan advancementsinmachinelearninganddeeplearningtechniquesforcropyieldpredictionacomprehensivereview |