Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning
Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk inje...
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MDPI AG
2024-11-01
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| author | Ittipon Khuimphukhieo Jose Carlos Chavez Chuanyu Yang Lakshmi Akhijith Pasupuleti Ismail Olaniyi Veronica Ancona Kranthi K. Mandadi Jinha Jung Juan Enciso |
| author_facet | Ittipon Khuimphukhieo Jose Carlos Chavez Chuanyu Yang Lakshmi Akhijith Pasupuleti Ismail Olaniyi Veronica Ancona Kranthi K. Mandadi Jinha Jung Juan Enciso |
| author_sort | Ittipon Khuimphukhieo |
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| description | Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus. |
| format | Article |
| id | doaj-art-937fa5d944cd418e914d5a1b4b5b6768 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-937fa5d944cd418e914d5a1b4b5b67682025-08-20T02:50:36ZengMDPI AGSensors1424-82202024-11-012423764610.3390/s24237646Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine LearningIttipon Khuimphukhieo0Jose Carlos Chavez1Chuanyu Yang2Lakshmi Akhijith Pasupuleti3Ismail Olaniyi4Veronica Ancona5Kranthi K. Mandadi6Jinha Jung7Juan Enciso8Texas A&M AgriLife Research & Extension Center, 2415 E. Highway 83, Weslaco, TX 78596, USATexas A&M AgriLife Research & Extension Center, 2415 E. Highway 83, Weslaco, TX 78596, USADepartment of Agriculture, Agribusiness and Environmental Sciences, Texas A&M University-Kingsville, Citrus Center, 312 N. International Blvd., Weslaco, TX 78596, USATexas A&M AgriLife Research & Extension Center, 2415 E. Highway 83, Weslaco, TX 78596, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USADepartment of Agriculture, Agribusiness and Environmental Sciences, Texas A&M University-Kingsville, Citrus Center, 312 N. International Blvd., Weslaco, TX 78596, USATexas A&M AgriLife Research & Extension Center, 2415 E. Highway 83, Weslaco, TX 78596, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USATexas A&M AgriLife Research & Extension Center, 2415 E. Highway 83, Weslaco, TX 78596, USAHuanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus.https://www.mdpi.com/1424-8220/24/23/7646greening diseaseantimicrobialsvegetation indicestexture features |
| spellingShingle | Ittipon Khuimphukhieo Jose Carlos Chavez Chuanyu Yang Lakshmi Akhijith Pasupuleti Ismail Olaniyi Veronica Ancona Kranthi K. Mandadi Jinha Jung Juan Enciso Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning Sensors greening disease antimicrobials vegetation indices texture features |
| title | Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning |
| title_full | Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning |
| title_fullStr | Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning |
| title_full_unstemmed | Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning |
| title_short | Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning |
| title_sort | assessing huanglongbing severity and canopy parameters of the huanglongbing affected citrus in texas using unmanned aerial system based remote sensing and machine learning |
| topic | greening disease antimicrobials vegetation indices texture features |
| url | https://www.mdpi.com/1424-8220/24/23/7646 |
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