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All Journal JOIV : International Journal on Informatics Visualization
Indrabayu, -
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Computer Science & IT

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An Eccentricity for Improvement in Rice Stem Borer Detection Using Sensed Drone Imaging Indrabayu, -; Basri, -; Achmad, Andani
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2864

Abstract

Rice stem borers are severe pests that cause significant crop losses. This research aimed to tackle this problem by using a drone equipped with a high-resolution camera to capture detailed images of paddy fields. These images were then processed to estimate the early potential attacks of stem borer pests through color segmentation computing. The detection process relied on analyzing color variations, particularly focusing on symptoms indicative of stem borer presence. The system utilized Hue, Saturation, Value (HSV) color segmentation and advanced image processing algorithms on numerous rice field videos collected from drone flights conducted at altitudes ranging from 5 to 40 meters above the ground. To improve detection accuracy, the study tested the system with and without the eccentricity parameter, which is crucial in eliminating false positives caused by the misidentification of field embankments as stem borers. This research's primary contribution is the implementation of eccentricity, which significantly reduces the false-positive rate. The results demonstrated that the accuracy of the system with the eccentricity parameter included was 75%, compared to a significantly lower accuracy rate of 17.19% when the eccentricity parameter was not used. Overall, this study highlights the effectiveness of using drones for remote sensing and the importance of incorporating eccentricity in image processing algorithms to enhance the precision of early stem borer detection in rice fields. This approach not only improves the reliability of pest detection but also offers a promising method for protecting rice crops from severe pest damage.
A Cascading of YOLOv8 and Random Forest Regression in Oil Palm Fresh Fruit Bunch Mass Estimation System using Unmanned Aerial Vehicle Imagery Indrabayu, -; Nurhadi, Muhammad Ijlal; Tandungan, Sofyan; Rahmat, Muhammad Abdillah
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3137

Abstract

Efficient management of oil palm farms requires accurate pre-harvest planning to maximize productivity. Traditional methods for estimating the mass of Fresh Fruit Bunches (FFBs) typically involve manual sampling and weighing, which are time-consuming and prone to errors. This study presents a novel system combining unmanned aerial vehicle (UAV) photography with geometric feature extraction using YOLOv8-Segmentation and machine learning models—Random Forest Regression (RFR)—to estimate FFB mass. The system addresses challenges posed by dynamic drone imagery, including environmental variations and frond occlusions. Instead of directly integrating YOLOv8 with the regression models, geometric features such as the minor axis, perimeter, and eccentricity are extracted from the segmented images and used to train the RFR for mass estimation. The top-performing model, using features extracted from YOLOv8-Small-Segmentation with the minor axis and eccentricity, achieved a Root Mean Square Error (RMSE) of 3.95 and a Mean Absolute Error (MAE) of 2.87 for frond-covered FFBs. For frond-uncovered FFBs, the best-performing features were the minor axis, perimeter, and area extracted using YOLOv8-Large-Segmentation, resulting in an RMSE of 3.91 and MAE of 2.91. These results demonstrate the system's capability to accurately estimate FFB mass based on UAV-captured imagery and feature extraction. This approach offers a scalable and efficient solution for pre-harvest planning in oil palm plantations, addressing the limitations of traditional methods while improving operational efficiency and accuracy in yield estimation.
Co-Authors Andani Achmad Basri, - Muhammad Abdillah Rahmat, Muhammad Abdillah Nurhadi, Muhammad Ijlal Tandungan, Sofyan