用于野生小麦病害诊断的可视化大语言模型

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-10-31 DOI:10.1016/j.compag.2024.109587
Kunpeng Zhang , Li Ma , Beibei Cui , Xin Li , Boqiang Zhang , Na Xie
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引用次数: 0

摘要

及早发现小麦植株的症状对于减轻病害影响和防止其蔓延至关重要。及时的植物检疫处理可最大限度地减少产量损失并提高治疗效果。近年来,基于图像分析的病害自动识别方法层出不穷,其中卷积神经网络(CNN)在视觉分类任务中取得了显著成功。现有的方法往往缺乏实际应用所需的智能和推理能力。本研究介绍了一种使用视觉语言模型(VLM)的先进小麦疾病诊断方法,命名为小麦疾病语言模型(WDLM)。WDLM 首先利用改进的 "分段任意模型"(SAM),从复杂的野生环境中分离出小麦的关键特征。为了提高逻辑推理能力,WDLM 整合了一个推理链,为其诊断生成清晰、合理的解释。通过采用专门的提示工程,本研究建立了小麦疾病语义数据集(WDSD),以对 VLM 进行微调。WDSD 包括一组来自不同来源的小麦图像,它在先进的 VLM 技术和小麦病理学之间架起了一座桥梁。WDLM 根据特定任务的数据进行了调整,通过对小麦病害进行准确分类并提出潜在的治疗方案,展示了卓越的智能。与基于 CNN 的模型、基于 Transformer 的模型和其他 VLM 相比,WDLM 在各种情况下都表现出更高的性能。WDLM 方法与移动应用相结合,可随时应用于现场,是小麦病害智能诊断领域的一大进步。
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Visual large language model for wheat disease diagnosis in the wild
Early detection of symptoms in wheat plants is crucial for mitigating disease effects and preventing their spread. Prompt phytosanitary treatment minimizes yield losses and enhances treatment efficacy. In recent years, numerous image analysis-based methodologies for automatic disease identification have been developed, with Convolutional Neural Networks (CNNs) achieving notable success in visual classification tasks. The existing methods often lack the necessary intelligence and reasoning for real-world applications. This study introduces an advanced wheat disease diagnosis approach using a Visual Language Model (VLM), named the Wheat Disease Language Model (WDLM). The WDLM first leverages the modified Segment Anything Model (SAM) to isolate key wheat features from complex wild environments. To enhance the logical reasoning abilities, the WDLM integrates a reasoning chain to generate clear, reasoned explanations for its diagnosis. By employing dedicated prompt engineering, this study establishes the Wheat Disease Semantic Dataset (WDSD) to fine-tune the VLM. The WDSD, which includes a diverse set of wheat images from various sources, bridges the gap between advanced VLM technology and wheat pathology. Tailored with task-specific data, the WDLM demonstrates superior intelligence by providing accurate classification of wheat diseases and suggesting potential treatment options. Compared to CNN-based models, Transformer-based models, and other VLMs, the WDLM shows improved performance in various scenarios. Integrated with mobile applications, the WDLM approach is readily applicable in the field, representing a promising advancement in the intelligent diagnosis of wheat diseases.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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