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Empowering Date Palm Disease Management with Deep Learning: A Comparative Performance Analysis of Pretrained Models for Stage-wise White-Scale Disease Classification

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Abdelaaziz Hessane ,
Abdelaaziz Hessane

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Mohamed Khalifa Boutahir ,
Mohamed Khalifa Boutahir

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Ahmed El Youssefi ,
Ahmed El Youssefi

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Yousef Farhaoui ,
Yousef Farhaoui

STI laboratory, T-IDMS, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Morocco

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Badraddine Aghoutane ,
Badraddine Aghoutane

IA Laboratory, Department of Computer Science, Faculty of Sciences, Moulay Ismail University of Meknès, Morocco

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Abstract

Deep Learning (DL) has revolutionized crop management practices, with disease detection and classification gaining prominence due to their impact on crop health and productivity. Addressing the limitations of traditional methods, such as reliance on handcrafted features, sensitivity to small datasets, limited adaptability, and scalability issues, deep learning enables accurate disease detection, real-time monitoring, and precision agriculture practices. Its ability to analyze and extract features from images, handle multimodal data, and adapt to new data patterns paves the way for a more sustainable and productive agricultural future. This study evaluates six pre-trained deep-learning models designed for stage-wise classification of white-scale date palm disease (WSD). The study assesses key metrics such as accuracy, sensitivity to training data volume, and inference time to identify the most effective model for accurate WSD stage-wise classification. For model development and assessment, we employed a dataset of 1,091 colored date palm leaflet images categorized into four distinct classes: healthy, low infestation degree, medium infestation degree, and high infestation degree. The results reveal the MobileNet model as the top performer, demonstrating superior accuracy and inference time compared to the other models and state of the art methods. The MobileNet model achieves high classification accuracy with only 60% of the training data. By harnessing the power of deep learning, this study enhances disease management practices in date palm agriculture, fostering improved crop yield, reduced losses, and sustainable food production.

How to Cite

1.
Hessane A, Khalifa Boutahir M, El Youssefi A, Farhaoui Y, Aghoutane B. Empowering Date Palm Disease Management with Deep Learning: A Comparative Performance Analysis of Pretrained Models for Stage-wise White-Scale Disease Classification. Data and Metadata [Internet]. 2023 Dec. 28 [cited 2024 Apr. 22];2:102. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/102

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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