Review on Crop Prediction Using Deep Learning Techniques

Dharani, M.; Thamilselvan, R.; Natesan, Prathiba; Kalaivaani, Pcd; Santhoshkumar, S.

doi:10.1088/1742-6596/1767/1/012026

Cited by 42 publications

(11 citation statements)

References 25 publications

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“…The authors of Ref. [18] conducted a survey study on the use of artificial intelligence for predicting agricultural yields, specifically focusing on deep learning and its various architectures. Their research highlighted a range of agricultural tasks that can be addressed using deep learning and identified a growing trend in the application of recurrent neural networks within the agriculture sector, especially for yield prediction.…”

Section: Comparison Of Related Work and Existing Surveys With The Pre...mentioning

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: Comparison Of Related Work and Existing Surveys With The Pre...mentioning

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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“…Dikshit et al [11] evaluate several machine learning approaches, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Extreme Learning Machines (ELM), decision trees, and random forests, for predicting droughts in various continents. A review by Dharani et al [9] delves into the use of deep learning for predicting crop yields, emphasizing classification and regression models tailored for this sector. Diaconu et al [10] apply the ConvLSTM network architecture to predict NDVI and RGB values in satellite imagery for land cover classification.…”

Section: D: Machine Learning and Neural Network For Geospatial Datamentioning

confidence: 99%

Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

Shevchenko,

Lukashevich,

Taniushkina

et al. 2024

IEEE Access

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The United Nations has identified improving food security and reducing hunger as essential components of its sustainable development goals. As of 2022, approximately 735 million people worldwide are experiencing hunger and malnutrition, with numerous fatalities reported. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages and subsequent social and political conflicts. To address this issue, we have developed a machine learning-based approach to predict the risk of substantial land suitability degradation and changes in irrigation patterns. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.

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“…Deep Learning Methods for Yield Estimation [13]. Automatic crop monitoring and yield prediction are now possible thanks to artificial intelligence.…”

Section: Fig 6convolutional Neural Networkmentioning

confidence: 99%

Convolutional and Spiking Neural Network Models for Crop Yield Forecasting

Karuna¹,

Pravallika²,

Anuradha³

et al. 2021

E3S Web Conf.

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Prediction of Crop yield focuses primarily on agriculture research which will have a significant effect on making decisions such as import-export, pricing and distribution of specific crops. Predicting accurately with well-timed forecasts is important, but it is a difficult task due to numerous complex factors. Mostly crops like wheat, rice, peas, pulses, sugar cane, tea, cotton, green houses, corn, and soybean can all be used to forecast crop yields. We considered corn dataset to predict the yield for 13 different states in United States. Crop development and progression are strongly affected by climatic changes and unpredictability. Predicting crop yield well before harvest time will support farmers for selling and storing their crops. Agriculture involves large datasets and knowledge processes. Factors such as Weather Components, Soil Components, Management practices, genotype and their interactions are used in predicting Corn Yield. Precise crop growth generally necessitates a complete overview of the functional correlations between yield and all these interactive variables, which necessitates the use of large datasets and complex algorithms to demonstrate. Various Machine Learning models, Deep Learning models, and Artificial Neural Network algorithms are used for predicting. Deep Neural Network Models such as Convolution Neural Networks (CNN), Spiking Neural Networks (SNN), and Recurrent Neural Networks (RNN) are used to assess corn yield. Integrating CNN, RNN and SNN models outperformed than individual model performance.

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Review on Crop Prediction Using Deep Learning Techniques

Cited by 42 publications

References 25 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

Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

Convolutional and Spiking Neural Network Models for Crop Yield Forecasting

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