Improved Optimization Algorithm in LSTM to Predict Crop Yield

Bhimavarapu, Usharani; Battineni, Gopi; Chintalapudi, Nalini

doi:10.3390/computers12010010

Cited by 25 publications

(7 citation statements)

References 29 publications

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“…The combined model, LSTM-RF, showed a better R-squared score of 0.71 than just LSTM model with R-squared score of 0.61. Other research works that utilized LSTM for improved crop yield prediction performance include [22][23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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“…Long short-term memory (LSTM) is a special recurrent neural network (RNN) that can solve the problem of multiple input variables well [20]. The LSTM model has a wide range of applications in different fields, such as finance, geology, meteorology, and so on [21][22][23][24]. It introduces a selective mechanism of "gating" based on RNN to selectively retain or delete information in order to better learn long-term dependencies.…”

Section: Construction Of a Carbon Dioxide Prediction Modelmentioning

confidence: 99%

Influencing the Variable Selection and Prediction of Carbon Emissions in China

Chang,

Jiao,

Wang

2023

Sustainability

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In order to study the changing rule of carbon dioxide emissions in China, this paper systematically focused on their current situation, influencing factors, and future trends. Firstly, the current situations of global carbon dioxide emissions and China’s carbon dioxide emissions were presented via a visualization method and their characteristics were analyzed; secondly, the random forest regression model was used to screen the main factors affecting China’s carbon emissions. Considering the different aspects of carbon emissions, 29 influencing factors were determined and 6 main influencing factors were determined according to the results of the random forest regression model. Then, a prediction model for carbon emissions in China was established. The BP neural network model, multi-factor LSTM time series model, and CNN-LSTM model were compared on the test set and all of them passed the test. However, the goodness of fit of the CNN-LSTM model was about 0.01~0.02 higher than the other two models and the MAE and RMSE of the CNN-LSTM model were about 0.01~0.03 lower than those of the other two models. Thus, it was selected to predict China’s carbon dioxide emissions. The predicted results showed that the peak of China’s carbon emissions will be around 2027 and the peak of these emissions will be between 12.9 billion tons and 13.2 billion tons. Overall, the paper puts forward reasonable suggestions for China’s low-carbon development and provides a reference for an adjustment plan of energy structure.

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“…To approach the analysis, several variants [14] were developed through Python libraries, Adadelta, is a variant of AdaGrad which takes the value of the accumulated gradient from the beginning of the execution which is restricted to the last gradients, RMSprop, uses the concept of a "window" to consider only the recent gradients, applying a weighted exponential mean to them to smooth the changes applied to the parameters. This approach can help prevent learning rates from becoming too small for better performance [15], Adam, is an algorithm based on fast descent and moments adapting to stochastic processes that allows searches in less time [16].…”

Section: Figure 7 Optimizers Used In Network Trainingmentioning

confidence: 99%

Flow Prediction for Hydropower Generation using LMST Neural Networks

Achig,

Gonzales,

Hidalgo

2023

J. Phys.: Conf. Ser.

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The objective of this investigation is to predict the necessary flow to satisfy the demand in order to guarantee the generation of energy in a hydropower, for which the artificial neural network LMST (Long Short-Term Memory) was used. In this context, in this article, historical data from the years 2010 to 2019 of flow and power of the plant was used, which were provided by it, in the first instance the data was purified to group them and graph the heat diagram with which the variables of interest were determined, then the training was carried out with which the network was structured with 10 neurons, 2 hidden layers, 2 freezing layers and 1 output layer. In addition, the RMSprop optimizer was selected with 10 numbers of delays with which an absolute error of 5.12% was obtained, finally tests were made comparing the results obtained in the training with data from one day after those that were used in the training and testing database. It is worth mentioning that by predicting the flow for the production of electrical energy, it contributes to the National Development Plan “Toda una Vida” of Ecuador, which is why it focuses on optimizing the use of water resources for required production levels.

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Improved Optimization Algorithm in LSTM to Predict Crop Yield

Cited by 25 publications

References 29 publications

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Influencing the Variable Selection and Prediction of Carbon Emissions in China

Flow Prediction for Hydropower Generation using LMST Neural Networks

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