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

Abdelaaziz Hessane, Mohamed Khalifa Boutahir, Ahmed El Youssefi, Yousef Farhaoui and Badraddine Aghoutane

Data and Metadata, 2023, vol. 2, 102

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

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:2:y:2023:i::p:102:id:1056294dm2023102

DOI: 10.56294/dm2023102

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().