Machine Learning Based Palm Farming: Harvesting and Disease Identification
In the culturally and economically vital date palm sector of the Arab world, precise assessment of fruit maturity, type, and disease is crucial for optimizing yield, quality, and palm health. This work pioneers a novel paradigm: machine learning (ML) frameworks for analysis of all three aspects usin...
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2024
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| Online Access: | https://hdl.handle.net/11073/25706 |
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| _version_ | 1864513437550247936 |
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| author | Khan, Sana Z. |
| author2 | Dhou, Salam Al-Ali, Abdul-Rahman |
| author2_role | author author |
| author_facet | Khan, Sana Z. Dhou, Salam Al-Ali, Abdul-Rahman |
| author_role | author |
| dc.creator.none.fl_str_mv | Khan, Sana Z. Dhou, Salam Al-Ali, Abdul-Rahman |
| dc.date.none.fl_str_mv | 2024-10-30T07:28:08Z 2024-10-30T07:28:08Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | S. Z. Khan, S. Dhou and A. R. Al-Ali, "Machine Learning Based Palm Farming: Harvesting and Disease Identification," in IEEE Access, doi: 10.1109/ACCESS.2024.3484943. 2169-3536 https://hdl.handle.net/11073/25706 10.1109/ACCESS.2024.3484943 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE Xplore |
| dc.subject.none.fl_str_mv | Disease identification Explainable AI Machine learning Smart farming Smart agriculture Yield estimation |
| dc.title.none.fl_str_mv | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| dc.type.none.fl_str_mv | Peer-Reviewed Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | In the culturally and economically vital date palm sector of the Arab world, precise assessment of fruit maturity, type, and disease is crucial for optimizing yield, quality, and palm health. This work pioneers a novel paradigm: machine learning (ML) frameworks for analysis of all three aspects using individual and merged datasets. Moreover, explainable AI (XAI) techniques are exploited to enhance result interpretability which has not been previously explored in this field. The purpose of this work is two-fold: 1) date fruit bunch type and ripeness classification, 2) classification of healthy and three stages of white-scale disease (WSD) infested date palm leaflets. For this purpose, we utilize deep learning (DL) models by adding additional layers and optimizing various parameters to enhance their performance for these specific tasks. Two publicly available datasets are used for both type and ripeness classification: Dataset 1 contains 8079 images, and Dataset 2 contains 9092 images of date fruit bunches. Furthermore, dataset 3 with 2161 images is used for healthy and WSD infestation stage identification. For individual datasets, the best performing model, VGG16, achieved the highest accuracy for date type classification (98%) and ripeness classification (93%), using dataset 1. The best performing classifier architecture on merged dataset, VGG16, achieved an accuracy of 97% and 94% for date fruit type and ripeness classification, respectively. The highest accuracy achieved for healthy and WSD classification was 99.7% using VGG16. These results were explained using several XAI techniques which were found to be useful in enhancing the models’ interpretability. Through this work, precision agriculture in the date palm sector stands to benefit from informed decision-making, optimized resource allocation, and the adoption of sustainable practices. This work contributes significantly to the sector's advancement, ensuring a thriving and resilient date palm industry in the region. |
| format | article |
| id | aus_8f3936e7719c8a341e8e0bbed81c43d6 |
| identifier_str_mv | S. Z. Khan, S. Dhou and A. R. Al-Ali, "Machine Learning Based Palm Farming: Harvesting and Disease Identification," in IEEE Access, doi: 10.1109/ACCESS.2024.3484943. 2169-3536 10.1109/ACCESS.2024.3484943 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25706 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE Xplore |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Machine Learning Based Palm Farming: Harvesting and Disease IdentificationKhan, Sana Z.Dhou, SalamAl-Ali, Abdul-RahmanDisease identificationExplainable AIMachine learningSmart farmingSmart agricultureYield estimationIn the culturally and economically vital date palm sector of the Arab world, precise assessment of fruit maturity, type, and disease is crucial for optimizing yield, quality, and palm health. This work pioneers a novel paradigm: machine learning (ML) frameworks for analysis of all three aspects using individual and merged datasets. Moreover, explainable AI (XAI) techniques are exploited to enhance result interpretability which has not been previously explored in this field. The purpose of this work is two-fold: 1) date fruit bunch type and ripeness classification, 2) classification of healthy and three stages of white-scale disease (WSD) infested date palm leaflets. For this purpose, we utilize deep learning (DL) models by adding additional layers and optimizing various parameters to enhance their performance for these specific tasks. Two publicly available datasets are used for both type and ripeness classification: Dataset 1 contains 8079 images, and Dataset 2 contains 9092 images of date fruit bunches. Furthermore, dataset 3 with 2161 images is used for healthy and WSD infestation stage identification. For individual datasets, the best performing model, VGG16, achieved the highest accuracy for date type classification (98%) and ripeness classification (93%), using dataset 1. The best performing classifier architecture on merged dataset, VGG16, achieved an accuracy of 97% and 94% for date fruit type and ripeness classification, respectively. The highest accuracy achieved for healthy and WSD classification was 99.7% using VGG16. These results were explained using several XAI techniques which were found to be useful in enhancing the models’ interpretability. Through this work, precision agriculture in the date palm sector stands to benefit from informed decision-making, optimized resource allocation, and the adoption of sustainable practices. This work contributes significantly to the sector's advancement, ensuring a thriving and resilient date palm industry in the region.American University of SharjahIEEE Xplore2024-10-30T07:28:08Z2024-10-30T07:28:08Z2024Peer-ReviewedPostprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfS. Z. Khan, S. Dhou and A. R. Al-Ali, "Machine Learning Based Palm Farming: Harvesting and Disease Identification," in IEEE Access, doi: 10.1109/ACCESS.2024.3484943.2169-3536https://hdl.handle.net/11073/2570610.1109/ACCESS.2024.3484943en_USoai:repository.aus.edu:11073/257062024-10-30T11:59:36Z |
| spellingShingle | Machine Learning Based Palm Farming: Harvesting and Disease Identification Khan, Sana Z. Disease identification Explainable AI Machine learning Smart farming Smart agriculture Yield estimation |
| status_str | publishedVersion |
| title | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| title_full | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| title_fullStr | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| title_full_unstemmed | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| title_short | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| title_sort | Machine Learning Based Palm Farming: Harvesting and Disease Identification |
| topic | Disease identification Explainable AI Machine learning Smart farming Smart agriculture Yield estimation |
| url | https://hdl.handle.net/11073/25706 |