2024
|
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. |
Andryan, Ricky; Junaedi, Ahmad; Purwono,; Prasetyo, Lilik B; Nurrahma, Arinal H I Optimizing cultivation system and pest management in different types of rice varieties Journal Article In: Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy), vol. 52, no. 2, pp. 176-186, 2024. @article{Andryan_Junaedi_Purwono_Prasetyo_IzzawatiNurrahma_2024,
title = {Optimizing cultivation system and pest management in different types of rice varieties},
author = {Ricky Andryan and Ahmad Junaedi and Purwono and Lilik B Prasetyo and Arinal H I Nurrahma},
url = {https://journal.ipb.ac.id/index.php/jurnalagronomi/article/view/54078},
doi = {https://10.24831/jai.v52i2.54078},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy)},
volume = {52},
number = {2},
pages = {176-186},
abstract = {Improving rice productivity and efficiency is the main goal of cultivation techniques to meet the demand for rice production. The aim of this research was to evaluate the suitability of rice varieties in cultivation systems, namely: organic cultivation + biological pest management (BPM), inorganic cultivation + BPM, and conventional cultivation. Different types of rice varieties, namely New Superior Varieties (NSV: INPARI-30, INPARI-32), New Plant Type (NPT: IPB-3S), and Superior Varieties from West Sumatra (Batang Piaman, Anak Daro), may exhibit differences in agronomic performance across various cultivation systems. This research was conducted at the Sawah Baru Babakan Experimental Farm, IPB University, Dramaga Bogor, using a factorial nested design with 4 replications. The results based on yield per hill and plot showed that Batang Piaman and INPARI-30 varieties were suitable for all organic + BPM, inorganic + BPM, and conventional cultivational systems. The INPARI-32 and IPB-3S varieties were more suitable for conventional cultivation and inorganic + BPM; while the Anak Daro variety was better in organic cultivation + BPM. Utilizing varieties with different characteristics for better yield performance could be considered with obtaining the suitability of the cultivation system, whether organic + BPM, inorganic + BPM, or conventional.},
keywords = {paddy, pest management, rice},
pubstate = {published},
tppubtype = {article}
}
Improving rice productivity and efficiency is the main goal of cultivation techniques to meet the demand for rice production. The aim of this research was to evaluate the suitability of rice varieties in cultivation systems, namely: organic cultivation + biological pest management (BPM), inorganic cultivation + BPM, and conventional cultivation. Different types of rice varieties, namely New Superior Varieties (NSV: INPARI-30, INPARI-32), New Plant Type (NPT: IPB-3S), and Superior Varieties from West Sumatra (Batang Piaman, Anak Daro), may exhibit differences in agronomic performance across various cultivation systems. This research was conducted at the Sawah Baru Babakan Experimental Farm, IPB University, Dramaga Bogor, using a factorial nested design with 4 replications. The results based on yield per hill and plot showed that Batang Piaman and INPARI-30 varieties were suitable for all organic + BPM, inorganic + BPM, and conventional cultivational systems. The INPARI-32 and IPB-3S varieties were more suitable for conventional cultivation and inorganic + BPM; while the Anak Daro variety was better in organic cultivation + BPM. Utilizing varieties with different characteristics for better yield performance could be considered with obtaining the suitability of the cultivation system, whether organic + BPM, inorganic + BPM, or conventional. |
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. |
