用于害虫检测的深度混合模型:基于物联网-无人机的智能农业系统

IF 1.1 4区 农林科学 Q3 PLANT SCIENCES
Vijayalakshmi Gokeda, Radhika Yalavarthi
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引用次数: 0

摘要

现代技术通过引入新的简化方法,正在彻底改变传统的耕作流程。尽管取得了这些进步,但疾病识别、昆虫检测和天气预报等挑战依然存在。为解决这些问题,本研究提出了一种基于 DHMPD 的物联网-无人机智能农业系统,重点关注害虫检测。该方法包括几个阶段:数据采集、预处理、数据增强、分割、特征提取和分类。在数据采集过程中,收集 "害虫数据集"。预处理包括 Z 值归一化,以生成更好的归一化图像。数据扩增包括旋转图像以创建不同的方向。分割阶段使用更新的 HDBSCAN 流程,利用混合欧氏距离和闵科夫斯基距离改进像素之间的距离计算。特征提取从分割后的图像中提取各种特征,包括修改后的 MBP 特征、基于颜色的特征和基于形状的特征。特征提取后,分类阶段采用混合技术和 DL 方法(如改进的 DBN 和 LSTM 方法)进行。最后,对分类结果进行平均,以准确预测害虫检测结果。通过各种评估对该方法的有效性进行了评价,旨在克服当前的局限性,增强智能农业系统。所提出的 DHMPD 方法与最先进的方法和传统分类器进行了比较,在准确检测害虫方面取得了 0.936 的最高准确率,优于传统方法。因此,所提出的工作为提高智能农业系统的能力带来了巨大的希望,提供了切实可行的解决方案,使农民、农业研究人员以及涉及作物管理和食品生产的行业从中受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Hybrid Model for Pest Detection: IoT-UAV-Based Smart Agriculture System

Modern technology is revolutionising traditional farming processes by introducing new and streamlined approaches. Despite these advancements, challenges such as disease identification, insect detection and weather forecasting persist. To address these issues, this work proposes a DHMPD-based IoT-UAV smart agriculture system focused on pest detection. The method involves several stages: data acquisition, preprocessing, data augmentation, segmentation, feature extraction and classification. During data acquisition, a ‘Pest data set’ is collected. Preprocessing includes Z-score normalisation to produce better-normalised images. Data augmentation involves rotating images to create different orientations. The segmentation stage uses an updated HDBSCAN process, which improves the distance calculation between pixels using hybridised Euclidean and Minkowski distances. Feature extraction retrieves various features from segmented images, including modified MBP features, colour-based features and shape-based features. After feature extraction, the classification phase is performed by a hybrid technique with DL approaches such as improved DBN and LSTM approaches. Finally, classification results are averaged to predict pest detection accurately. The approach's effectiveness is evaluated through various assessments, aiming to overcome current limitations and enhance smart agriculture systems. The proposed DHMPD method was compared with state-of-the-art approaches and traditional classifiers, achieving a maximum accuracy of 0.936, outperforming conventional methods in accurately detecting pests. Hence, the proposed work holds immense promise to advance the capabilities of smart agriculture systems, offering practical solutions that can benefit farmers, agricultural researchers and industries involved in crop management and food production.

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来源期刊
Journal of Phytopathology
Journal of Phytopathology 生物-植物科学
CiteScore
2.90
自引率
0.00%
发文量
88
审稿时长
4-8 weeks
期刊介绍: Journal of Phytopathology publishes original and review articles on all scientific aspects of applied phytopathology in agricultural and horticultural crops. Preference is given to contributions improving our understanding of the biotic and abiotic determinants of plant diseases, including epidemics and damage potential, as a basis for innovative disease management, modelling and forecasting. This includes practical aspects and the development of methods for disease diagnosis as well as infection bioassays. Studies at the population, organism, physiological, biochemical and molecular genetic level are welcome. The journal scope comprises the pathology and epidemiology of plant diseases caused by microbial pathogens, viruses and nematodes. Accepted papers should advance our conceptual knowledge of plant diseases, rather than presenting descriptive or screening data unrelated to phytopathological mechanisms or functions. Results from unrepeated experimental conditions or data with no or inappropriate statistical processing will not be considered. Authors are encouraged to look at past issues to ensure adherence to the standards of the journal.
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