Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8

Ahmad Fauzi, Kiki Ahmad Baihaqi, Anggun Pertiwi, Yudo Devianto, Saruni Dwiasnati
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Abstract

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
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利用 Yollo v8 中的卷积神经网络基于图像检测水稻叶片害虫
水稻叶片害虫的检测在农业中非常重要,因为它可以帮助农民确定适当的预防措施。数字图像处理技术是检测稻叶害虫的一种方法。在本研究中,对卷积神经网络(CNN)算法与 R-CNN 和 YOLOv8 的离线运行进行了验证,以检测稻叶害虫。在数据准备阶段,从不同来源获取了水稻叶片图像,其中 100 张图像来自网站数据,10 张图像来自研究现场。然后,进行预处理和数据扩增,以提高图像质量和增加数据变化。在模型训练阶段,使用 R-CNN 和 YOLOv8 两种算法进行训练和评估。使用 Yolov8 对相同数据进行测试的准确率为 87.0%,精确率为 79%;而使用 R-CNN 进行测试的准确率为 85%,精确率为 75%,数据分为 80 个训练数据、20 个验证数据和 10 个测试数据。对数据集进行标记时使用的是已完全标准化的 Makesensei,所得到的参数是水稻叶片上的斑点。
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