DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting

Gavahi, Keyhan; Abbaszadeh, Peyman; Moradkhani, Hamid

doi:10.1016/j.eswa.2021.115511

Cited by 78 publications

(42 citation statements)

References 32 publications

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“…A 3D-CNN can extract both spatial and spectral features at the same time, which can provide more specific extracted features in comparison with 2D-CNN [4]. The used 3D-CNN includes a 3×3×1 kernel size, with 128 filter size, followed by batch normalization.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, machine learning techniques were used for accurate prediction models [3]. Indeed, it results in food availability in the future, and also the product resources demand can be used optimally [4]. Moreover, digital and intelligence farming with the usage of remote sensing data leads the farmers to get closer to new advanced innovation methods.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Nejad

Abbasi‐Moghadam

Sharifi

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, national economies are highly affected by Crop Yield Predictions. By early prediction, the market price can be predicted, importing and exporting plan can be provided, social and economic effects of waste products can be minimized, and a program can be presented for humanitarian food aid. In addition, agricultural fields are constantly growing to generate products required. The use of machine learning methods in this sector can lead to the efficient production and highquality agricultural products. Traditional predictive machine models were unable to find non-linear relationships between data. Recently, there has been a revolution in prediction systems via the advancement of machine learning which can be used to achieve highly accurate decision-making networks. Thus far, many strategies have been used to evaluate agricultural products, such as DeepYield, CNN-LSTM, and ConvLSTM. However, preferable prediction accuracy is required. In this study, two architectures have been proposed. The first model includes 2D-CNN, skip connections, and LSTM-Attentions. The second model comprises 3D-CNN, skip connections, and ConvLSTM Attention. The Input data given from MODIS products such as Land-Cover, Surface-Temperature, and MODIS-Land-surface from 2003 to 2018 on the county level over 1800 counties where soybean is mainly cultivated in the USA. The proposed methods have been compared with the most recent models. Then, the results showed that the second proposed method notably outperformed the other techniques. In case of MAE, the second proposed method,

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Nejad

Abbasi‐Moghadam

Sharifi

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…ANNs are a kind of AI that mimics the human brain ( Gavahi, Abbaszadeh & Moradkhani, 2021 ). Densely coupled neurons, the network architecture, and the learning technique influence a neural network’s function.…”

Section: Methodsmentioning

confidence: 99%

“…Multimodal data fusion is a key component of this process. Using 3D-CNN and Conv-LSTM networks together, Gavahi, Abbaszadeh & Moradkhani (2021) came up with a deep yielding technique. A DNN architecture with feature fusion at the input and intermediate levels was utilized to forecast agricultural production by Maimaitijiang et al (2019) .…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Al-Adhaileh

Aldhyani

2022

PeerJ Computer Science

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Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Analyses of datasets for crop yields, temperature, and insecticides led to the development of the MLP models. The datasets are randomly divided into separate samples, 70% for training and 30% for testing. The best-performing MLP model is characterized by values of (R = 100%) and (R2 = 96.33) for predicting insecticides in the testing process. The temperature, insecticides, and rainfall were examined with different crop yields to confirm the effectiveness of these parameters for increasing product crop yields in Saudi Arabia; we found that these items had highest relationships. The average values are R = 98.20%, 96.50, and 99.14% with for the temperature, insecticides, and rainfall, respectively. Based on these findings, it appeared that each of the parameter categories that are considered (temperature, pesticides, and rainfall) had a similar contribution to the accuracy of anticipated yield projection.

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“…Wheat forecast production is accurately predicted by using LSTM model [47]. Plant growth variation and forecast yield production in tomato and Ficus benjamina stem growth by LSTM showed promising results in the controlled environments [48]. LSTM showed a great ability to disclose phenological properties, while DCNN has a great ability to extract more spatial features [49].…”

Section: Introductionmentioning

confidence: 99%

Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon

Chang

Hameed

et al. 2021

Horticulturae

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In precision agriculture, the nitrogen level is significantly important for establishing phenotype, quality and yield of crops. It cannot be achieved in the future without appropriate nitrogen fertilizer application. Moreover, a convenient and real-time advance technology for nitrogen nutrition diagnosis of crops is a prerequisite for an efficient and reasonable nitrogen-fertilizer management system. With the development of research on plant phenotype and artificial intelligence technology in agriculture, deep learning has demonstrated a great potential in agriculture for recognizing nondestructive nitrogen nutrition diagnosis in plants by automation and high throughput at a low cost. To build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse. The digital images of canopy leaves and the environmental factors (light and temperature) during the growth period of muskmelons were tracked and analyzed. The nitrogen concentrations of the plants were measured, we successfully constructed and trained machine-learning- and deep-learning models based on the traditional backpropagation neural network (BPNN), the emerging convolution neural network (CNN), the deep convolution neural network (DCNN) and the long short-term memory (LSTM) for the nitrogen nutrition diagnosis of muskmelon. The adjusted determination coefficient (R2) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R2 = 0.567 and MSE = 0.429 for BPNN model; R2 = 0.376 and MSE = 0.628 for CNN model; R2 = 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R2 = 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.

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DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting

Cited by 78 publications

References 32 publications

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia

Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon

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