2024
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Wijayanto, Arif K; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Hongo, Chiharu Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics Journal Article In: Smart Agricultural Technology, vol. 10, iss. March 2025, 2024. @article{nokey,
title = {Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics},
author = {Arif K Wijayanto and Lilik B Prasetyo and Sahid A Hudjimartsu and Chiharu Hongo},
url = {https://doi.org/10.1016/j.atech.2024.100766},
doi = {https://doi.org/10.1016/j.atech.2024.100766},
year = {2024},
date = {2024-12-30},
urldate = {2024-12-30},
journal = {Smart Agricultural Technology},
volume = {10},
issue = {March 2025},
abstract = {This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance.},
keywords = {drone, patch fragmentation, rice},
pubstate = {published},
tppubtype = {article}
}
This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance. |
Wijayanto, Arif K; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Hongo, Chiharu Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy Journal Article In: Smart Agricultural Technology, vol. 8, iss. August 2024, 2024. @article{nokey,
title = {Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy},
author = {Arif K Wijayanto and Lilik B Prasetyo and Sahid A Hudjimartsu and Chiharu Hongo},
url = {https://doi.org/10.1016/j.atech.2024.100498},
doi = {10.1016/j.atech.2024.100498},
year = {2024},
date = {2024-06-28},
urldate = {2024-06-28},
journal = {Smart Agricultural Technology},
volume = {8},
issue = {August 2024},
abstract = {Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management.},
keywords = {drone, haralick, paddy, rice, textural feature},
pubstate = {published},
tppubtype = {article}
}
Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management. |
2020
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Rahman, Dede Aulia; Setiawan, Yudi; Wijayanto, Arif K; Aziz, Ahmad Abdul; Martiyani, Trisna Rizky An experimental approach to exploring the feasibility of unmanned aerial vehicle and thermal imaging in terrestrial and arboreal mammals research Conference vol. 211, E3S Web Conf., 2020, ISSN: 2267-1242. @conference{Rahman2020,
title = {An experimental approach to exploring the feasibility of unmanned aerial vehicle and thermal imaging in terrestrial and arboreal mammals research},
author = {Dede Aulia Rahman and Yudi Setiawan and Arif K Wijayanto and Ahmad Abdul Aziz and Trisna Rizky Martiyani},
url = {https://www.e3s-conferences.org/articles/e3sconf/abs/2020/71/e3sconf_jessd2020_02010/e3sconf_jessd2020_02010.html},
doi = {10.1051/e3sconf/202021102010},
issn = {2267-1242},
year = {2020},
date = {2020-11-25},
volume = {211},
publisher = {E3S Web Conf.},
abstract = {The visual camouflage of many species living in the dense cover of the tropical rainforest become obstacles to conducting species monitoring. Unmanned aerial vehicles (drones) combined with thermal infrared imaging (TIR) can rapidly scan large areas from above and detect wildlife that has a body temperature that contrasts with its surrounding environment. This research tested the feasibility of DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to detect terrestrial and arboreal mammals in the five tree density classes in the remaining natural environment on the IPB University campus. This study demonstrated that large-size terrestrial mammal thermal signatures are visible in sparse vegetation at daytime and in the area under the canopy at night monitoring. In contrast, arboreal mammals were better detected in at early morning and night. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. The drones allow safe operation at low altitudes with low levels of disturbance to animals. Both terrestrial and arboreal mammals are well detected and easily identified when the drone is flying at an altitude < 50 m HAGL. Our preliminary results indicated that thermal surveys from drones are a promising method.},
keywords = {drone, UAV},
pubstate = {published},
tppubtype = {conference}
}
The visual camouflage of many species living in the dense cover of the tropical rainforest become obstacles to conducting species monitoring. Unmanned aerial vehicles (drones) combined with thermal infrared imaging (TIR) can rapidly scan large areas from above and detect wildlife that has a body temperature that contrasts with its surrounding environment. This research tested the feasibility of DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to detect terrestrial and arboreal mammals in the five tree density classes in the remaining natural environment on the IPB University campus. This study demonstrated that large-size terrestrial mammal thermal signatures are visible in sparse vegetation at daytime and in the area under the canopy at night monitoring. In contrast, arboreal mammals were better detected in at early morning and night. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. The drones allow safe operation at low altitudes with low levels of disturbance to animals. Both terrestrial and arboreal mammals are well detected and easily identified when the drone is flying at an altitude < 50 m HAGL. Our preliminary results indicated that thermal surveys from drones are a promising method. |
Rahman, Dede Aulia; Setiawan, Yudi; Wijayanto, Arif K; Aziz, Ahmad Abdul; Martiyani, Trisna Rizky Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results Conference vol. 211, E3S Web Conf., 2020, ISSN: 2267-1242. @conference{Rahman2020b,
title = {Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results},
author = {Dede Aulia Rahman and Yudi Setiawan and Arif K Wijayanto and Ahmad Abdul Aziz and Trisna Rizky Martiyani},
url = {https://www.e3s-conferences.org/articles/e3sconf/abs/2020/71/e3sconf_jessd2020_04007/e3sconf_jessd2020_04007.html},
doi = {10.1051/e3sconf/202021104007},
issn = {2267-1242},
year = {2020},
date = {2020-11-25},
volume = {211},
publisher = {E3S Web Conf.},
abstract = {Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime.},
keywords = {drone, UAV},
pubstate = {published},
tppubtype = {conference}
}
Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime. |
2019
|
Wijayanie, Akira; Setiawan, Yudi; Hikmat, Agus; Pairah,; Septiana, Wardi; Erlan, Mochamad; Hilmy, Yoesri Characterization of vegetation structure in Gunung Halimun Salak National Park corridor with drone technology and Geographic Information System (GIS) Conference vol. 11372, SPIE, 2019. @conference{Wijayanie2019,
title = {Characterization of vegetation structure in Gunung Halimun Salak National Park corridor with drone technology and Geographic Information System (GIS)},
author = {Akira Wijayanie and Yudi Setiawan and Agus Hikmat and Pairah and Wardi Septiana and Mochamad Erlan and Yoesri Hilmy},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/2539337/Characterization-of-vegetation-structure-in-Gunung-Halimun-Salak-National-Park/10.1117/12.2539337.short},
doi = {10.1117/12.2539337},
year = {2019},
date = {2019-12-28},
volume = {11372},
publisher = {SPIE},
abstract = {Gunung Halimun Salak National Park (GHSNP) corridor is an area that connects Salak and Halimun Mountain, and has a role in animal movement, breeding and living. This study aims to characterize the vegetation structure in a restoration area in the corridor of Gunung Halimun Salak National Park. The vegetation characteristics was analyzed through structural vegetation datasets such as Canopy Height Model (CHM) and some vegetation indices namely; Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI). Significance of the approach was evaluated by the Mann Whitney test. The results indicated that the restoration area of HSNPC consist of seedlings, saplings, poles and trees. GHSNP’s corridor canopy layer consists of five canopy layers, namely strata A (> 30 m), B (20 – 30 m), C (4 – 20 m), D (1 – 4 m), and E (0 – 1 m). The most important species are Schima wallichii, Agathis dammara, Bellucia axinanthera and Macaranga triloba. The effective vegetation index to see the differences vegetation structure are NDVI and RVI vegetation index.},
keywords = {characterization, drone, vegetation structure},
pubstate = {published},
tppubtype = {conference}
}
Gunung Halimun Salak National Park (GHSNP) corridor is an area that connects Salak and Halimun Mountain, and has a role in animal movement, breeding and living. This study aims to characterize the vegetation structure in a restoration area in the corridor of Gunung Halimun Salak National Park. The vegetation characteristics was analyzed through structural vegetation datasets such as Canopy Height Model (CHM) and some vegetation indices namely; Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI). Significance of the approach was evaluated by the Mann Whitney test. The results indicated that the restoration area of HSNPC consist of seedlings, saplings, poles and trees. GHSNP’s corridor canopy layer consists of five canopy layers, namely strata A (> 30 m), B (20 – 30 m), C (4 – 20 m), D (1 – 4 m), and E (0 – 1 m). The most important species are Schima wallichii, Agathis dammara, Bellucia axinanthera and Macaranga triloba. The effective vegetation index to see the differences vegetation structure are NDVI and RVI vegetation index. |
