TY - JOUR AU - Dwi Pupitasari, Trismayanti AU - Basori, Ahmad AU - Yufit Riskiawan, Hendra AU - Sarwo Setyohadia, Dwi Putro Sarwo AU - Agus Kurniasari, Arvita AU - Firgiyanto, Refa AU - Firdausiah Mansur, Andi Besse AU - Yunianta, Arda PY - 2022/06/27 Y2 - 2024/03/28 TI - Intelligent Detection of Rice Leaf Diseases Based on Histogram Color and Closing Morphological JF - Emirates Journal of Food and Agriculture JA - Emir J Food Agric VL - 34 IS - 5 SE - Research Article DO - 10.9755/ejfa.2022.v34.i5.2858 UR - https://ejfa.me/index.php/journal/article/view/2858 SP - AB - <p>Harvest drop in rice because of leaf blast is a vital issue in the country’s food stock and social life where rice is the primary source of <br>food. Epidemics can cause leaf blasts due to weather conditions or environmental transformation. Therefore, early detection of leaf blast is <br>needed to take precautions action to save the harvest. This research presents a new approach for rice leaf blast detection. It seizes colour <br>distribution and shapes to determine the damaging leaf. Two main features: colour and shape, are key points to measure the similarity of <br>an image by comparing the image query and database. The image extraction uses histogram colour throughout the pre-processing phase. <br>The approach will take the dominant colour of leaf. Since this green colour dominated the leaf, the green will be converted from RGB to <br>the HSV domain with 256 range. The shape feature extraction based on morphology closing will calculate the images’ area, diameter, <br>and perimeter. The process is continued by resizing the image and convert into a grayscale mode to apply canny edge detection. The <br>experiment uses 267 images dataset and 74 testing data consisting of 2 categories: blast disease leaf and healthy leaf. The trial results <br>achieve an 85.71% accuracy rate to detect blast disease by colour feature, 71.42% by shape feature, and 85.71% by combined colourshape features</p> ER -