Prof. Dr. Ir. Lilik Budi Prasetyo, M.Sc

@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}

}

@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}

}