Crop diagnostic system: A robust disease detection and management system for leafy green crops grown in an aquaponics facility

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
R. Abbasi , P. Martinez , R. Ahmad
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引用次数: 1

Abstract

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle, just like traditional agriculture. The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system. Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security, reduce water scarcity, and eliminate carbon footprint. For the large-scale implementation of this farming technique, a unified system is needed that can detect crop diseases and support researchers and farmers in identifying potential causes and treatments at early stages. This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops, lettuce, basil, spinach, and parsley, grown in an aquaponics facility. First, a dataset comprising 2640 images is constructed. Then, a disease detection system is developed that works in three phases. The first phase is a crop classification system that identifies the type of crop. The second phase is a disease identification system that determines the crop's health status. The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease. The proposed approach has shown promising results with accuracy in each of the three phases, reaching 95.83%, 94.13%, and 82.13%, respectively. The final disease detection system is then integrated with an ontology model through a cloud-based application. This ontology model contains domain knowledge related to crop pathology, particularly causes and treatments of different diseases of the studied leafy green crops, which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures. The proposed application finds its significance as a decision support system that can automate aquaponics facility health monitoring and assist agricultural practitioners in decision-making processes regarding crop and disease management.

作物诊断系统:一个强大的疾病检测和管理系统,用于水培设施中种植的绿叶作物
与传统农业一样,水培农场种植的作物在生长周期中容易受到各种疾病或生物胁迫的影响。早期发现疾病对于见证水培系统的效率和进步至关重要。水产养殖结合了循环水产养殖和无土水培方法,有望确保粮食安全,减少水资源短缺,消除碳足迹。为了大规模实施这项农业技术,需要一个统一的系统来检测作物疾病,并支持研究人员和农民在早期阶段识别潜在的原因和治疗方法。这项研究提出了一种自动作物诊断系统,用于检测在水培设施中种植的四种绿叶作物(莴苣、罗勒、菠菜和欧芹)的生物胁迫和控制疾病。首先,构建包括2640个图像的数据集。然后,开发了一个分三个阶段工作的疾病检测系统。第一阶段是确定作物类型的作物分类系统。第二阶段是确定作物健康状况的疾病识别系统。最后一个阶段是疾病检测系统,该系统定位和检测叶片中的病变和健康斑点,并对疾病进行分类。所提出的方法在三个阶段中的每一个阶段都显示出有希望的结果,准确率分别达到95.83%、94.13%和82.13%。然后通过基于云的应用程序将最终的疾病检测系统与本体模型集成。该本体模型包含与作物病理学相关的领域知识,特别是所研究的叶绿作物的不同疾病的原因和治疗,这些知识可以在疾病检测时自动提取,从而使农业从业者能够采取预防措施。所提出的应用程序作为一个决策支持系统具有重要意义,可以自动化水产养殖设施的健康监测,并帮助农业从业者进行作物和疾病管理的决策过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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