Deep-LSTM Model for Wheat Crop Yield Prediction in India

Saini, Preeti; Nagpal, Bharti

doi:10.1109/ccict56684.2022.00025

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…The effectiveness of the LSTM in capturing patterns in time series data for crop detection has been demonstrated in numerous case studies worldwide. For example, studies in India [67,68] and Brazil [69] used LSTM to classify different crop types, and they achieved better results than other machine learning algorithms. Similarly, a study in the United States combined remote sensing data with deep learning algorithms, including LSTMs, to achieve high accuracy in crop mapping [70].…”

Section: Discussionmentioning

confidence: 99%

Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

Gafurov,

Mukharamova,

Saveliev

et al. 2023

Agriculture

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This study addresses the challenge of accurate crop detection using satellite data, focusing on the application of Long Short-Term Memory (LSTM) networks. The research employs a “spatial generalization” approach, where a model trained on one geographic area is applied to another area with similar vegetation conditions during the growing season. LSTM networks, which are capable of learning long-term temporal dependencies, are used to overcome the limitations of traditional machine learning techniques. The results indicate that LSTM networks, although more computationally expensive, provide a more accurate solution for crop recognition compared with other methods such as Multilayer Perceptron (MLP) and Random Forest algorithms. The accuracy of LSTM networks was found to be 93.7%, which is significantly higher than the other methods. Furthermore, the study showed a high correlation between the real and model areas of arable land occupied by different crops in the municipalities of the study area. The main conclusion of this research is that LSTM networks, combined with a spatial generalization approach, hold great promise for future agricultural applications, providing a more efficient and accurate tool for crop recognition, even in the face of limited training data and complex environmental variables.

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Section: Discussionmentioning

confidence: 99%

Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

Gafurov,

Mukharamova,

Saveliev

et al. 2023

Agriculture

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“…They involve several mathematical equations that are used to compute the output of each layer. LSTM: It is a form of RNN [24] which incorporates gating mechanisms to selectively retain or discard information from previous time steps. This makes it well-suited for processing long sequences of data and modeling long-term dependencies.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Crop yield prediction using deep learning algorithm based on CNN-LSTM with Attention Layer and Skip Connection

Dharwadkar,

Kalmani,

Thapa

2023

Preprint

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Accurate prediction of crop production is of utmost importance in the agricultural sector, as it allows farmers and decision-makers to plan and optimize their resources effectively. Numerous factors, including weather conditions, soil moisture, and temperature, have a direct impact on crop yield. To address this challenge, this paper proposes a hybrid model that combines 1D CNN and LSTMwith an attention layer, aiming to provide precise crop production forecasting. The study targets on the prediction of two frequently cultivated crops in India, namely wheat and rice. These crops were chosen due to their significant contribution to the agricultural landscape of the country. Different modification were donein CNN-LSTM to achieve the higher accuracy and at last CNN-LSTM multi-head attention with multiplication skip connection was modified. The suggested hybrid model was compared to conventional machine learning methods in order to assess its efficiency, including Support Vector Regressor, Decision Tree Regressor, and Random Forest Regressor. The experiments’ findings illustrate how the modified hybrid model outperforms other models and previous studies, achieving impressive performance metrics. The RMSE value of 0.017 indicates the small margin of error in the predicted crop production. The MAE value of 0.09 further emphasizes the accuracy of the model’s predictions. Additionally, the achieved R2 value of 0.967 signifies a high level of correlation between the predicted and actual crop production

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Forecasting Wheat Yield Using Long Short- Term Memory Considering Soil and Metrological Parameters

Babbar

Kumar

Verma

2023

2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)

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Deep-LSTM Model for Wheat Crop Yield Prediction in India

Cited by 5 publications

References 9 publications

Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

Crop yield prediction using deep learning algorithm based on CNN-LSTM with Attention Layer and Skip Connection

Forecasting Wheat Yield Using Long Short- Term Memory Considering Soil and Metrological Parameters

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