Crop Yield Prediction Using Deep Learning in Mediterranean Region

Fathi, Maroi Tsouli; Ezziyyani, Mostafa; Mamoune, Soumaya El

doi:10.1007/978-3-030-36664-3_12

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…In agricultural context, besides having been used for crop classification and identification [74][75][76], DL techniques have played an important role in areas such as detecting diseases [77][78][79][80][81], yield prediction [82][83][84][85][86] and weed detection [14,[87][88][89] and have also shown great potential in detecting agricultural abandonment using remote sensing data [17,34].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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AGRI-PREDI Prediction System of Climate Change Based on Machine Learning for Precision Agriculture in Mediterranean Region

Fathi

Khrouch

et al. 2023

Lecture Notes in Networks and Systems

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Big data and predictive analytics: A systematic review of applications

Jamarani,

Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

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Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

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Crop Yield Prediction Using Deep Learning in Mediterranean Region

Cited by 4 publications

References 4 publications

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

AGRI-PREDI Prediction System of Climate Change Based on Machine Learning for Precision Agriculture in Mediterranean Region

Big data and predictive analytics: A systematic review of applications

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