Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

Yli-Heikkilä, Maria; Wittke, Samantha; Luotamo, Markku; Puttonen, Eetu; Sulkava, Mika; Pellikka, Petri; Heiskanen, Janne; Klami, Arto

doi:10.3390/rs14174193

Cited by 17 publications

(10 citation statements)

References 78 publications

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“…Temporal Convolutional Networks (TCNs): This architecture applies causal convolutions adapting to the temporal data by considering their sequential nature [129]. In their study, the authors of [100] developed a TCN-based prediction model using satellite imagery, focusing on the prediction of various crops, such as wheat, barley, and oats. Their findings indicate that the model based on TCN exceeds traditional machine learning methods and yields more precise values.…”

Section: Transformersmentioning

confidence: 99%

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Meghraoui,

Sebari,

Pilz

et al. 2024

Technologies

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Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.

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Section: Transformersmentioning

confidence: 99%

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Meghraoui,

Sebari,

Pilz

et al. 2024

Technologies

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“…Machine learning has the ability to analyze data for hidden patterns and connections. ML includes various techniques such as Ridge Regression (RR) [4], Regression Tree (RT) [5], Support Vector Machine (SVM) [6], XGBoost [7], Convolutional Neural Network (CNN) [8], Random Forests (RF), and K-Nearest Neighbor (KNN) and Deep Neural Network have all been used for crop detection and yield forecasting of specific crops in various contexts [9][10][11]. The literature on these strategies has been thoroughly examined and debated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The network learns the association among input units and expected output feedback by adjusting the weight and bias parameters. As a result, the MLP network anticipated output for the ℎ 𝑡ℎ neuron with the 𝑚 𝑡ℎ the node can assess using the formula shown in Equation (11).…”

Section: Multi-layer Perceptron Modelmentioning

confidence: 99%

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Ahmed

2023

Sustainability

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Predicting crop yields is one of agriculture’s most challenging issues. It is crucial in making national, provincial, and regional choices and estimates the government to meet the food demands of its citizens. Crop production is anticipated based on various factors such as soil conditions and meteorological, environmental, and crop variables. This study intends to develop an effective model that can accurately anticipate agricultural production in advance, assisting farmers in better planning. In the current study, the Crop Yield Prediction Dataset is normalized initially, and then feature engineering is performed to determine the significance of the feature in assessing the crop yield. Crop yield forecasting is performed using the Multi-Layer Perceptron model and the Spider Monkey Optimization method. The Multi-Layer Perceptron technique is efficient in dealing with the non-linear relations among the features in the data, and the Spider Monkey Optimization technique would assist in optimizing the corresponding feature weights. The current study uses data from the Food and Agriculture Organization and the World Data Bank to forecast maize yield in the Saudi Arabia region based on factors such as average temperature, average rainfall, and Hg/Ha production in past years. The suggested MLP-SMO model’s prediction effectiveness is being evaluated using several evaluation metrics such as Root-Mean-Square Error, R-Squared, Mean Absolute Error, and Mean Bias Error, where the model has outperformed in the prediction process with a Root-Mean-Square Error value of 0.11, which is lowest among all the techniques that are considered in the statical analysis in the current study.

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“…Furthermore, recent progress on artificial intelligence has opened new ways to improve the yield estimation from remotely sensed data. Traditional machine learning approaches are integrated by deep learning and temporal convolutional networks (TCNs) that outperform the classical machine learning method (e.g., neural network, random forest) and have been successfully applied [37].…”

Section: Introductionmentioning

confidence: 99%

Time Series from Sentinel-2 for Organic Durum Wheat Yield Prediction Using Functional Data Analysis and Deep Learning

Mancini,

Solfanelli,

Coviello

et al. 2024

Agronomy

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Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting system by combining vegetation index time-series data from Sentinel-2 L2A time-series data, field-measured yields, and deep learning techniques. Remotely sensed data over a season could be, in general, noisy and characterized by a variable density due to weather conditions. This problem was mitigated using Functional Principal Component Analysis (FPCA). We obtained a functional representation of acquired data, and starting from this, we tried to apply deep learning to predict the crop yield. We used a Convolutional Neural Network (CNN) approach, starting from images that embed temporal and spectral dimensions. This representation does not require one to a priori select a vegetation index that, typically, is task-dependent. The results have been also compared with classical approaches as Partial Least Squares (PLS) on the main reference vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge index (NDRE), considering both in-season and end-season scenarios. The obtained results show that the image-based representation of multi-spectral time series could be an effective method to estimate the yield, also, in the middle stage of cropping with R2 values greater than 0.83. The developed model could be used to estimate yield the neighbor fields characterized by similar setups in terms of the crop, variety, soil, and, of course, management.

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Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

Cited by 17 publications

References 78 publications

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Time Series from Sentinel-2 for Organic Durum Wheat Yield Prediction Using Functional Data Analysis and Deep Learning

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