Empowering Date Palm Disease Management with Deep Learning: A Comparative Performance Analysis of Pretrained Models for Stage-wise White-Scale Disease Classification
DOI:
https://doi.org/10.56294/dm2023102Keywords:
Precision Agriculture, Date Palm (Phoenix Dactylifera), White-Scale Disease, Deep LearningAbstract
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
References
1. Abbas M, Hafeez F, Ali A, Farooq M, Latif M, Saleem M, Ghaffar A. Date palm white scale (Parlatoria blanchardii T): a new threat to date industry in Pakistan. J Entomol Zool Stud. 2014;2(6):49-52.
2. Ahmed M, Ahmed A. Palm tree disease detection and classification using residual network and transfer learning of inception ResNet. PLOS ONE. 2023;18(3):e0282250. https://doi.org/10,1371/journal.pone.0282250.
3. Alaa H, Waleed K, Samir M, Tarek M, Sobeah H, Salam MA. An intelligent approach for detecting palm trees diseases using image processing and machine learning. Int J Adv Comput Sci Appl. 2020;11(7):434-441. https://doi.org/10,14569/IJACSA.2020,0110757.
4. Albahar M. A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities. Agriculture. 2023;13(3):540. https://doi.org/10,3390/agriculture13030540.
5. Al-gaashani MSAM, Shang F, Muthanna MSAM, Khayyat M, Abd El-Latif AA. Tomato leaf disease classification by exploiting transfer learning and feature concatenation. IET Image Process. 2022;16(3):913-925. https://doi.org/10,1049/ipr2,12397.
6. Al-Shalout M, Mansour K. Detecting Date Palm Diseases Using Convolutional Neural Networks. 2022:1-5. https://doi.org/10,1109/acit53391,2021.9677103.
7. Attallah O. Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection. Horticulturae. 2023;9(2):149. https://doi.org/10,3390/horticulturae9020149.
8. Borugadda P, Lakshmi R, Sahoo S. Transfer Learning VGG16 Model for Classification of Tomato Plant Leaf Diseases: A Novel Approach for Multi-Level Dimensional Reduction. 2023;31(2):813-841. https://doi.org/10,47836/pjst.31,2.09.
9. Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. http://arxiv.org/abs/1610,02357.
10. Date Palm data | Kaggle. (n.d.). Retrieved January 30, 2022, from https://www.kaggle.com/hadjerhamaidi/date-palm-data.
11. Deng J, Dong W, Socher R, Li L-J, Kai Li, Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009:248-255. https://doi.org/10,1109/CVPR.2009,5206848.
12. Domingues T, Brandão T, Ferreira JC. Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey. Agriculture. 2022;12(9):1350. https://doi.org/10,3390/agriculture12091350.
13. Emmanuel A, Mwangi RW, Murithi P, Fredrick K, Danison T. Classification of cassava leaf diseases using deep Gaussian transfer learning model. https://doi.org/10,1002/eng2,12651.
14. Fan X, Luo P, Mu Y, Zhou R, Tjahjadi T, Ren Y. Leaf image based plant disease identification using transfer learning and feature fusion. Comput Electron Agric. 2022. https://doi.org/10,1016/j.compag.2022,106892.
15. Gibril MBA, Shafri HZM, Shanableh A, Al-Ruzouq R, Wayayok A, Hashim SJ. Deep convolutional neural network for large-scale date palm tree mapping from uav-based images. Remote Sensing. 2021;13(14). https://doi.org/10,3390/rs13142787.
16. Gulzar Y, Ünal Z, Aktaş H, Mir MS. Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study. Agriculture. 2023;13(8):1479. https://doi.org/10,3390/agriculture13081479.
17. Hadipour-Rokni R, Askari Asli-Ardeh E, Jahanbakhshi A, Esmaili paeen-Afrakoti I, Sabzi S. Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique. Comput Biol Med. 2023;155:106611. https://doi.org/10,1016/j.compbiomed.2023,106611.
18. Hessane A, Boutahir MK, El Youssefi A, Farhaoui Y, Aghoutane B. Deep-PDSC: A Deep Learning-Based Model for a Stage-Wise Classification of Parlatoria Date Scale Disease. In: ; 2023:207-219. https://doi.org/10,1007/978-3-031-25662-2_17.
19. Hessane A, Boutahir MK, El Youssefi A, Farhaoui Y, Aghoutane B. Deep-PDSC: A Deep Learning-Based Model for a Stage-Wise Classification of Parlatoria Date Scale Disease. In: ; 2023:207-219. https://doi.org/10,1007/978-3-031-25662-2_17.
20. Hessane A, EL Youssefi A, Farhaoui Y, Aghoutane B, Qaraai Y. Artificial Intelligence Applications in Date Palm Cultivation and Production: A Scoping Review. In: ; 2023:230-239. https://doi.org/10,1007/978-3-031-26254-8_32.
21. Hessane A, EL Youssefi A, Farhaoui Y, Aghoutane B, Qaraai Y. Artificial Intelligence Applications in Date Palm Cultivation and Production: A Scoping Review. In: ; 2023:230-239. https://doi.org/10,1007/978-3-031-26254-8_32.
22. Hessane A, Youssefi AE, Farhaoui Y, Aghoutane B, Amounas F. A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease. Big Data Mining and Analytics. https://doi.org/10,26599/
23. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. 2017.
24. Karar ME, Reyad O, Abdel-Aty A-H, Owyed S, Hassan MF. Intelligent IoT-Aided early sound detection of red palm weevils. Comput Mater Continua. 2021;69(3):4095-4111. https://doi.org/10,32604/cmc.2021,019059.
25. Kaur P, Harnal S, Gautam V, Singh MP, Singh SP. A novel transfer deep learning method for detection and classification of plant leaf disease. J Ambient Intell Humaniz Comput. 2023;14(9):12407-12424. https://doi.org/10,1007/s12652-022-04331-9.
26. Liu G, Peng J, El-Latif AAA. SK-MobileNet: A Lightweight Adaptive Network Based on Complex Deep Transfer Learning for Plant Disease Recognition. Arabian J Sci Eng. 2023;48(2):1661-1675. https://doi.org/10,1007/s13369-022-06987-z.
27. Magsi A, Mahar JA, Razzaq MA, Gill SH. Date Palm Disease Identification Using Features Extraction and Deep Learning Approach. In: 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020. https://doi.org/10,1109/INMIC50486,2020.9318158.
28. Nayak A, Chakraborty S, Swain DK. Application of smartphone-image processing and transfer learning for rice disease and nutrient deficiency detection. Smart Agric Technol. 2023;4:100195. https://doi.org/10,1016/j.atech.2023,100195.
29. Nigam S, Jain R, Marwaha S, Arora A, Haque MA, Dheeraj A, Singh VK. Deep transfer learning model for disease identification in wheat crop. Ecol Inform. 2023;75:102068. https://doi.org/10,1016/j.ecoinf.2023,102068.
30. Osco-Mamani EF, Chaparro-Cruz IN. Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Perú. 2023;14(4):851-860. https://doi.org/10,14569/IJACSA.2023,0140494.
31. Primary Industries and Regions SA (PIRSA) - Government of South Australia. Parlatoria Date Scale Parlatoria blanchardii [Fact sheet]. 2020.
32. Sandhya Devi RS, Vijay Kumar VR, Sivakumar P. EfficientNetV2 Model for Plant Disease Classification and Pest Recognition. 2023;45(2):2249-2263. https://doi.org/10,32604/csse.2023,032231.
33. Shahoveisi F, Taheri Gorji H, Shahabi S, Hosseinirad S, Markell S, Vasefi F. Application of image processing and transfer learning for the detection of rust disease. Sci Rep. 2023;13(1):5133. https://doi.org/10,1038/s41598-023-31942-9.
34. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. https://doi.org/10,48550/arXiv.1409,1556.
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Copyright (c) 2023 Abdelaaziz Hessane, Mohamed Khalifa Boutahir, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane (Author)
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