Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India
Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Em...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1976091 |
| _version_ | 1797684875998789632 |
|---|---|
| author | Nishu Bali Anshu Singla |
| author_facet | Nishu Bali Anshu Singla |
| author_sort | Nishu Bali |
| collection | DOAJ |
| description | Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work. |
| first_indexed | 2024-03-12T00:36:05Z |
| format | Article |
| id | doaj.art-576e8927668540728c79cac38df8fba3 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:05Z |
| publishDate | 2021-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-576e8927668540728c79cac38df8fba32023-09-15T09:33:59ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452021-12-0135151304132810.1080/08839514.2021.19760911976091Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North IndiaNishu Bali0Anshu Singla1Chitkara University Institute of Engineering & Technology, Chitkara UniversityChitkara University Institute of Engineering & Technology, Chitkara UniversityCrop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work.http://dx.doi.org/10.1080/08839514.2021.1976091 |
| spellingShingle | Nishu Bali Anshu Singla Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India Applied Artificial Intelligence |
| title | Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India |
| title_full | Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India |
| title_fullStr | Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India |
| title_full_unstemmed | Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India |
| title_short | Deep Learning Based Wheat Crop Yield Prediction Model in Punjab Region of North India |
| title_sort | deep learning based wheat crop yield prediction model in punjab region of north india |
| url | http://dx.doi.org/10.1080/08839514.2021.1976091 |
| work_keys_str_mv | AT nishubali deeplearningbasedwheatcropyieldpredictionmodelinpunjabregionofnorthindia AT anshusingla deeplearningbasedwheatcropyieldpredictionmodelinpunjabregionofnorthindia |