Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco
This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality cons...
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MDPI AG
2025-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1106 |
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| author | Mohamed Arame Issam Meftah Kadmiri Francois Bourzeix Yahya Zennayi Rachid Boulamtat Abdelghani Chehbouni |
| author_facet | Mohamed Arame Issam Meftah Kadmiri Francois Bourzeix Yahya Zennayi Rachid Boulamtat Abdelghani Chehbouni |
| author_sort | Mohamed Arame |
| collection | DOAJ |
| description | This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists of the application of these techniques to chickpea plants in controlled laboratory conditions using a natural infestation protocol, something not previously explored. The two major methodologies adopted in the approach are as follows: (1) spectral feature-based classification using hyperspectral data within the 400–1000 nm range, wherein a random forest classifier is trained to classify a plant as healthy or infested with eggs or larvae. Dimensionality reduction methods such as principal component analysis (PCA) and kernel principal component analysis (KPCA) were tried, and the best classification accuracies (over 80%) were achieved. (2) VI-based classification, leveraging indices associated with plant health, such as NDVI, EVI, and GNDVI. A support vector machine and random forest classifiers effectively classified healthy and infested plants based on these indices, with over 81% classification accuracies. The main objective was to design an integrated early pest detection framework using advanced imaging and machine learning techniques. Results show that both approaches have resulted in high classification accuracy, highlighting the potential of this approach in precision agriculture for timely pest management interventions. |
| format | Article |
| id | doaj-art-84d3d80a31354016b502f6da7221d787 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-84d3d80a31354016b502f6da7221d7872025-08-20T03:47:48ZengMDPI AGAgronomy2073-43952025-04-01155110610.3390/agronomy15051106Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in MoroccoMohamed Arame0Issam Meftah Kadmiri1Francois Bourzeix2Yahya Zennayi3Rachid Boulamtat4Abdelghani Chehbouni5Department of Plant, Biotechnology and Soil Sciences (PBS), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoDepartment of Plant, Biotechnology and Soil Sciences (PBS), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoAnalytics-Lab, Mohammed VI Polytechnic University (UM6P), Rabat 43150, MoroccoAnalytics-Lab, Mohammed VI Polytechnic University (UM6P), Rabat 43150, MoroccoDepartment of Entomology, International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat P.O. Box 6299, MoroccoCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoThis study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists of the application of these techniques to chickpea plants in controlled laboratory conditions using a natural infestation protocol, something not previously explored. The two major methodologies adopted in the approach are as follows: (1) spectral feature-based classification using hyperspectral data within the 400–1000 nm range, wherein a random forest classifier is trained to classify a plant as healthy or infested with eggs or larvae. Dimensionality reduction methods such as principal component analysis (PCA) and kernel principal component analysis (KPCA) were tried, and the best classification accuracies (over 80%) were achieved. (2) VI-based classification, leveraging indices associated with plant health, such as NDVI, EVI, and GNDVI. A support vector machine and random forest classifiers effectively classified healthy and infested plants based on these indices, with over 81% classification accuracies. The main objective was to design an integrated early pest detection framework using advanced imaging and machine learning techniques. Results show that both approaches have resulted in high classification accuracy, highlighting the potential of this approach in precision agriculture for timely pest management interventions.https://www.mdpi.com/2073-4395/15/5/1106chickpealeaf minerhyperspectral imagingpest detectionmachine learning |
| spellingShingle | Mohamed Arame Issam Meftah Kadmiri Francois Bourzeix Yahya Zennayi Rachid Boulamtat Abdelghani Chehbouni Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco Agronomy chickpea leaf miner hyperspectral imaging pest detection machine learning |
| title | Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco |
| title_full | Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco |
| title_fullStr | Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco |
| title_full_unstemmed | Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco |
| title_short | Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco |
| title_sort | detection of leaf miner infestation in chickpea plants using hyperspectral imaging in morocco |
| topic | chickpea leaf miner hyperspectral imaging pest detection machine learning |
| url | https://www.mdpi.com/2073-4395/15/5/1106 |
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