Information Processing in Agriculture最新文献_第5页

Environmental assessment of industrial aquaponics in arid zones using an integrated dynamic model 基于综合动态模型的干旱区工业水培环境评价

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-06-01 Epub Date: 2024-09-19 DOI: 10.1016/j.inpa.2024.09.005

Ze Zhu , Uri Yogev , Amit Gross , Karel J. Keesman

Land desertification, water scarcity, and food security challenges in arid zones are intensifying, driving the need for sustainable agricultural solutions like aquaponics. This study investigated innovative water and energy-saving strategies using an integrated dynamic model for an on-demand industrial aquaponics system in Israel. The model evaluated the performance of a recirculating aquaculture system (RAS), hydroponics system (HPS), and desalination unit (DU) by adjusting physical and operational parameters to optimize water and nutrient use efficiency, energy consumption, and yield. Optimizing the system design resulted in an aquaponics system with approximately 420 m³ RAS, 6.85 ha HPS and 40 m³/d DU, achieving phosphorus use efficiency of 96 %, a water use efficiency of 97 %, freshwater input of 1.5 L/day/m², and energy consumption of 0.56 kWh/day/m². To mitigate the challenges of extreme arid climates, evaporative cooling combined with outdoor shading and mechanical cooling was found to be a feasible option to control temperature and humidity in the greenhouse. Dehumidification technologies further improved system performance by recovering 22 % freshwater from seawater and increasing nitrogen use efficiency by 18 %. Achieving daily energy self-sufficiency required 4500 m² photovoltaic panels and 5000 m² solar heating system. While the system model was initially devised with a specific focus on conditions in Israel, it has been designed with scalability, allowing it to be adapted and applied extensively across diverse peri-urban regions and arid zones globally.

干旱地区的土地荒漠化、水资源短缺和粮食安全挑战正在加剧，推动了对水培等可持续农业解决方案的需求。本研究利用以色列按需工业水培系统的综合动态模型调查了创新的水和节能策略。该模型通过调整物理和操作参数，对循环水养殖系统（RAS）、水培系统（HPS）和海水淡化装置（DU）的性能进行评估，以优化水和养分利用效率、能耗和产量。优化后的水培系统RAS为420 m3， HPS为6.85 ha， DU为40 m3/d，磷利用效率为96%，水利用效率为97%，淡水输入量为1.5 L/d /m2，能耗为0.56 kWh/d /m2。为了缓解极端干旱气候的挑战，蒸发冷却结合室外遮阳和机械冷却被认为是控制温室温度和湿度的可行选择。除湿技术进一步提高了系统性能，从海水中回收22%的淡水，提高了18%的氮利用效率。实现每日能源自给需要4500平方米的光伏板和5000平方米的太阳能供暖系统。虽然该系统模型最初是专门针对以色列的情况设计的，但它的设计具有可扩展性，可以在全球不同的城市周边地区和干旱地区进行调整和广泛应用。

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引用次数: 0

Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques 用于水稻叶片病害检测的深度学习：关于新兴趋势、方法和技术的系统文献综述

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-06-01 Epub Date: 2024-05-08 DOI: 10.1016/j.inpa.2024.04.006

Chinna Gopi Simhadri , Hari Kishan Kondaveeti , Valli Kumari Vatsavayi , Alakananda Mitra , Preethi Ananthachari

Rice is an essential food crop that is cultivated in many countries. Rice leaf diseases can cause significant damage to crop cultivation, leading to reduced yields and economic losses. Traditional disease detection approaches are often time-consuming, labor-intensive, and require expertise. Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference. Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques. Image processing techniques are used to extract features from diseased leaf images, such as the color, texture, vein patterns, and shape of lesions. Machine learning techniques are used to detect diseases based on the extracted features. In contrast, deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks. This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection, such as Transfer Learning, Ensemble Learning, and Hybrid approaches. This review also discusses the effectiveness of these approaches in addressing various challenges. This review discusses the details of various models and hyperparameter settings used, model fine-tuning techniques followed, and performance evaluation metrics utilized in various studies. This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.

水稻是许多国家种植的重要粮食作物。水稻叶片病害可对作物栽培造成重大损害，导致产量下降和经济损失。传统的疾病检测方法往往耗时耗力，而且需要专业知识。自动叶片病害检测方法帮助农民在没有或较少人为干扰的情况下检测病害。早期对水稻叶片病害检测的研究大多依赖于图像处理和机器学习技术。利用图像处理技术从病变叶片图像中提取特征，如病变的颜色、纹理、静脉模式和形状。基于提取的特征，使用机器学习技术检测疾病。相比之下，深度学习技术从大型数据集中学习复杂的模式，没有明确的特征提取技术，非常适合疾病检测任务。本系统综述探讨了文献中用于水稻叶片病害检测的各种深度学习方法，如迁移学习、集成学习和混合方法。本综述还讨论了这些方法在应对各种挑战方面的有效性。这篇综述讨论了各种模型和使用的超参数设置的细节，模型微调技术，以及各种研究中使用的性能评估指标。本文还讨论了现有研究的局限性，并提出了进一步开发更强大、更有效的水稻叶病检测技术的未来方向。

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引用次数: 0

Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree 基于分层EMD阶段特征的决策树方法对水稻病虫害和营养缺乏症进行分类鉴定

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-06-01 Epub Date: 2024-09-10 DOI: 10.1016/j.inpa.2024.09.003

A. Pushpa Athisaya Sakila Rani , N. Suresh Singh

Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.

病虫害、病害和营养缺乏是制约水稻产量的主要因素。因此，本文提出了一种鉴定病虫害和营养缺乏症的分类体系。该方法首先使用熵滤波对叶片图像进行预处理，然后进行叶片分割处理。然后在叶子图像上构建多层，通过多层提取特征。采用灰度共生矩阵（GLCM）算法和主成分分析（PCA）方法提取叶片图像的全局纹理特征。在每一层上构造一维信号序列，通过经验模态分解算法对其进行分解，并从中估计相位特征。使用决策树分类器对特征进行训练/分类，决策树分类器对害虫攻击、疾病发病率和营养缺乏类别进行分类。该方法的精密度、准确度、特异性、敏感性和f1评分分别为97%、97.88%、96.52%、96.7%和96.7%。

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引用次数: 0

Vine yield estimation from block to regional scale employing remote sensing, weather, and management data 利用遥感、天气和管理数据估算从块到区域的葡萄产量

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-06-01 Epub Date: 2024-06-26 DOI: 10.1016/j.inpa.2024.06.001

Pedro C. Towers , Sean E. Roulet , Carlos Poblete-Echeverría

Knowledge of the spatial variation in vine yield at different scales is crucial for the wine business, and combined with estimations of vine size variability enables within-block mapping of vegetative-reproductive balance. Remote sensing combined with other data that excludes field sampling appears as an optimal approach for yield estimation for a broad range of scales. In this study, mean yield and factors known to affect yield components were collected for over 8000 blocks, over 18 seasons, in the western oasis of Mendoza, Argentina. Partial Least Squares (PLS) and Random Forest (RF) models were used to analyse the relationship between these factors and yield. The PLS model delivered very poor results, with coefficients of determination lower than 0.08. RF models with 49 to 19 variables produced predictions with coefficients of determination of 0.96 to 0.90, respectively. Some factors traditionally considered important in yield determination, such as trellis, frost occurrence, or planting density had limited influence, whereas location weighed heavily. Results suggest a successful approach to spatial prediction of yield that requires no fieldwork and indicates VRB mapping at block-scale may be possible with these tools. Several improvements to inputs are proposed.

了解不同尺度下葡萄产量的空间变化对葡萄酒行业至关重要，结合对葡萄大小变化的估计，可以在块内绘制植物-生殖平衡图。遥感与不包括实地抽样的其他数据相结合，似乎是在大比例尺范围内估计产量的最佳方法。本研究收集了阿根廷门多萨西部绿洲18个季节8000多个区块的平均产量和已知影响产量组成部分的因素。采用偏最小二乘（PLS）和随机森林（RF）模型分析了这些因素与产量的关系。PLS模型提供了非常差的结果，决定系数低于0.08。具有49至19个变量的RF模型产生的预测的决定系数分别为0.96至0.90。一些传统上被认为对产量决定很重要的因素，如棚架、霜冻发生或种植密度的影响有限，而位置的影响很大。研究结果表明，不需要实地工作就可以成功地进行产量空间预测，并表明使用这些工具可以在块尺度上绘制VRB地图。对投入提出了若干改进建议。

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引用次数: 0

An automatic method for estimating insect defoliation with visual highlights of consumed leaf tissue regions 一种利用消耗叶片组织区域的视觉亮点估算昆虫落叶情况的自动方法

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-03-01 Epub Date: 2024-03-02 DOI: 10.1016/j.inpa.2024.03.001

Gabriel S. Vieira , Afonso U. Fonseca , Naiane Maria de Sousa , Julio C. Ferreira , Juliana Paula Felix , Christian Dias Cabacinha , Fabrizzio Soares

As an essential component of the architecture of a plant, leaves are crucial to sustaining decision-making in cultivars and effectively support agricultural processes. When the leaf area is constantly monitored, a plant’s health and productive capacity can be assessed to foment proactive and reactive strategies. Because of that, one of the most critical tasks in agricultural processes is estimating foliar damage. In this sense, we present an automatic method to estimate leaf stress caused by insect herbivory, including damage in border regions. As a novelty, we present a method with well-defined processing steps suitable for numerical analysis and visual inspection of defoliation severity. We describe the proposed method and evaluate its performance concerning 12 different plant species. Experimental results show high assertiveness in estimating leaf area loss with a concordance correlation coefficient of 0.98 for grape, soybean, potato, and strawberry leaves. A classic pattern recognition approach, named template matching, is at the core of the method whose performance is compared to cutting-edge techniques. Results demonstrated that the method achieves foliar damage quantification with precision comparable to deep learning models. The code prepared by the authors is publicly available.

作为植物结构的重要组成部分，叶片对维持品种决策和有效支持农业进程至关重要。当不断监测叶面积时，可以评估植物的健康和生产能力，以促进主动和被动策略。正因为如此，农业过程中最关键的任务之一是估计叶面损害。在这个意义上，我们提出了一种自动估计昆虫食草性叶片胁迫的方法，包括边界地区的损害。作为一种新颖的方法，我们提出了一种具有明确定义的处理步骤的方法，适用于数值分析和落叶严重程度的目视检查。我们描述了所提出的方法，并评估了其在12种不同植物物种上的性能。实验结果表明，葡萄、大豆、马铃薯和草莓叶片的叶面积损失估计具有较高的自信，一致性相关系数为0.98。该方法的核心是一种经典的模式识别方法，即模板匹配方法，其性能与前沿技术相比较。结果表明，该方法达到了与深度学习模型相当的精度。作者编写的代码是公开的。

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引用次数: 0

A deep learning framework for prediction of crop yield in Australia under the impact of climate change 预测气候变化影响下澳大利亚作物产量的深度学习框架

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-03-01 Epub Date: 2024-04-18 DOI: 10.1016/j.inpa.2024.04.004

Haydar Demirhan

Accurate prediction of crop yields is essential to ensure food security. In this study, a new deep neural networks framework is developed to predict crop yields in Australia, considering the impact of climate change, fertilizer use, and crop area. It is implemented for oats, corn, rice, and wheat crops, and its forecasting performance is benchmarked against five statistical and machine learning methods. All the software codes for the implementation of the proposed framework are freely available. The proposed framework shows the highest forecasting performance for all the considered crop types. It provides 23%, 38%, 39%, and 40% lower average mean absolute error than the benchmark methods for oat, corn, rice, and wheat crops, respectively. The reductions in average root mean squared error are 19%, 25%, 37%, and 29% over the benchmark methods. Then, it is used to predict yields of the considered crops in Australia towards 2025 under six different climate change scenarios. It is observed that although climate change has some boosting impact on crop yield, it is not sustainable to meet the demand. However, it is possible to keep crop yields rising while mitigating climate change.

准确预测作物产量对确保粮食安全至关重要。在这项研究中，我们开发了一个新的深度神经网络框架来预测澳大利亚的作物产量，同时考虑了气候变化、肥料使用和作物面积的影响。它适用于燕麦、玉米、大米和小麦作物，其预测性能是根据五种统计和机器学习方法进行基准测试的。实现所提议的框架的所有软件代码都是免费提供的。建议的框架对所有考虑的作物类型显示出最高的预测性能。与燕麦、玉米、水稻和小麦的基准方法相比，该方法的平均绝对误差分别降低了23%、38%、39%和40%。与基准方法相比，平均均方根误差降低了19%、25%、37%和29%。然后，它被用来预测在六种不同的气候变化情景下，到2025年澳大利亚所考虑的作物的产量。我们观察到，气候变化虽然对作物产量有一定的促进作用，但满足需求是不可持续的。然而，在减缓气候变化的同时保持作物产量的增长是可能的。

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引用次数: 0

Technologies, Protocols, and applications of Internet of Things in greenhouse Farming: A survey of recent advances 温室种植中的物联网技术、协议和应用：最新进展概览

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-03-01 Epub Date: 2024-04-12 DOI: 10.1016/j.inpa.2024.04.002

Khalid M. Hosny , Walaa M. El-Hady , Farid M. Samy

Greenhouse farming is considered one of the precision and sustainable forms of smart agriculture. Although greenhouse gases can support off-season crops inside the indoor environment, monitoring, controlling, and managing crop parameters at greenhouse farms more precisely and securely is necessary, even in harsh climate regions. The evolving Internet of Things (IoT) technologies, including smart sensors, devices, network topologies, big data analytics, and intelligent decision-making, are thought to be the solution for automating greenhouse farming parameters like internal atmosphere control, irrigation control, crop growth monitoring, and so on. This paper introduces a comprehensive survey of recent advances in IoT-based greenhouse farming. We summarize the related review articles. The classification of greenhouse farming based on IoT (smart greenhouse, hydroponics greenhouse, and vertical farming) is introduced. Also, we present a detailed architecture for the components of greenhouse agriculture applications based on IoT, including physical devices, communication protocols, and cloud/fog computing technologies. We also present a classification of IoT applications of greenhouse farming, including monitoring, controlling, tracking, and predicting. Furthermore, we present the technical and resource management challenges for optimal greenhouse farming. Moreover, countries already applying IoT in greenhouse farming have been presented. Lastly, future suggestions related to IoT-based greenhouse farming have been introduced.

温室农业被认为是智能农业的一种精确和可持续的形式。尽管温室气体可以在室内环境中支持淡季作物，但更精确、更安全地监测、控制和管理温室农场的作物参数是必要的，即使在气候恶劣的地区也是如此。不断发展的物联网（IoT）技术，包括智能传感器、设备、网络拓扑、大数据分析和智能决策，被认为是自动化温室农业参数的解决方案，如内部大气控制、灌溉控制、作物生长监测等。本文全面介绍了物联网温室农业的最新进展。我们对相关的综述文章进行了总结。介绍了基于物联网的温室农业分类（智能温室、水培温室、垂直农业）。此外，我们还提出了基于物联网的温室农业应用组件的详细架构，包括物理设备，通信协议和云/雾计算技术。我们还对温室农业的物联网应用进行了分类，包括监测、控制、跟踪和预测。此外，我们提出了优化温室农业的技术和资源管理挑战。此外，还介绍了已经在温室农业中应用物联网的国家。最后，对未来物联网温室农业的发展提出了建议。

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引用次数: 0

Society 5.0 enabled agriculture: Drivers, enabling technologies, architectures, opportunities, and challenges 社会 5.0 支持农业：驱动因素、使能技术、架构、机遇和挑战

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-03-01 Epub Date: 2024-04-16 DOI: 10.1016/j.inpa.2024.04.003

Kossi Dodzi Bissadu, Salleh Sonko, Gahangir Hossain

The existing agriculture practices faced many challenges and fail to address some of the most critical needs of the growing population. Food insecurity, high initial cost of smart farming, severe farm labor shortage worldwide, economic, social, and political crises related to famines, poverty, climate change, and the technology focus of Agriculture 4.0 calls for rethinking the agriculture paradigm. Moreover, the idea of Society 5.0 promoted by Japanese government triggered many position reactions from policymakers, governments, private institutions, academicians, and researchers. The idea of human centered society where individuals live their lives to the fullest with shared vision of happiness, social harmony, sustainability, and resilience recently caught scholars’ attention. Several researchers investigated the society 5.0 and its critical components including Agriculture 5.0. Agriculture 5.0 not only could be leveraged to address many existing issues, but could become a major driving force for achieving Society 5.0’s goals. This paper follows a systematic literature review approach to investigate the major drivers, enabling cutting-edge technologies, various opportunities and challenges for developing, adopting, and implementation Agriculture 5.0. It also highlighted the overall and holistic architectural framework based on 12 layers of Agriculture 5.0 paradigm. Though Agriculture 5.0 is promising with many opportunities, such as creating new job opportunities for young generations, and boosting mass customization, it will face many potential challenges. Some challenges include cybersecurity and privacy issues, difficulties for an effective legal, regulatory and compliance system due to high automation and mass personalization, standardization issues, and adapting agricultural production strategies and models to constantly changing customer preferences.

现有的农业实践面临许多挑战，无法满足不断增长的人口的一些最关键的需求。粮食不安全、智能农业的高初始成本、全球范围内严重的农业劳动力短缺、与饥荒、贫困、气候变化相关的经济、社会和政治危机以及农业4.0的技术重点要求我们重新思考农业范式。此外，日本政府提出的社会5.0理念引发了政策制定者、政府、私人机构、学者和研究人员的许多立场反应。最近，以人为中心的社会概念引起了学者们的关注，在这个社会中，每个人都有对幸福、社会和谐、可持续性和弹性的共同愿景，从而充分享受自己的生活。一些研究人员调查了社会5.0及其关键组成部分，包括农业5.0。农业5.0不仅可以用来解决许多现有的问题，而且可以成为实现社会5.0目标的主要推动力。本文采用系统的文献综述方法，研究了农业5.0的主要驱动因素、前沿技术、发展、采用和实施农业5.0的各种机遇和挑战。强调了基于12层农业5.0范式的整体整体架构框架。“农业5.0”虽然为年轻一代创造新的就业机会、促进大规模定制等带来了很多机会，但也面临着许多潜在的挑战。一些挑战包括网络安全和隐私问题，由于高度自动化和大规模个性化，难以建立有效的法律、监管和合规系统，标准化问题，以及使农业生产战略和模式适应不断变化的客户偏好。

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引用次数: 0

Few-shot cow identification via meta-learning 通过元学习进行奶牛识别

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-03-01 Epub Date: 2024-04-10 DOI: 10.1016/j.inpa.2024.04.001

Xingshi Xu, Yunfei Wang, Yuying Shang, Guangyuan Yang, Zhixin Hua, Zheng Wang, Huaibo Song

Cow identification is a prerequisite for precision livestock farming. Biometric-based methods have made significant progress in cow identification. However, substantial labelling costs and frequent identification task changes are still hamper model application. In this work, a novel method called “MFCI” was proposed to achieve accurate cow identification under few-shot and task-changing conditions. Specifically, the proposed method comprises two components: cow location and cow identification. First, an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images. Then, the Model-Agnostic Meta-Learning (MAML) framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows. Moreover, an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches. The experimental results showed that the proposed cow location model achieved a mAP of 99.5 %. The proposed cow identification model attained an accuracy of 90.43 % with only five samples per cow for 20 cows, outperforming other state-of-the-art methods. The results demonstrate the broad applicability and significant value of the proposed method.

牛的鉴定是精确畜牧业的先决条件。基于生物特征的方法在奶牛识别方面取得了重大进展。然而，大量的标签成本和频繁的识别任务变化仍然阻碍了模型的应用。在这项工作中，提出了一种称为“MFCI”的新方法，以实现在少量射击和任务变化条件下准确识别奶牛。具体来说，该方法包括两个部分：奶牛定位和奶牛识别。首先，采用改进的带有Ghost模块的YOLOv5n快速检测图像中的奶牛位置。然后，引入了模型不可知元学习（Model-Agnostic Meta-Learning， MAML）框架，以便在少量条件下准确识别，并快速适应奶牛个体的频繁变化。此外，采用了自动编码器，通过结合监督和无监督方法，使Base-Learner能够学习更多的广义特征。实验结果表明，所提出的奶牛定位模型的mAP值达到了99.5%。所提出的奶牛识别模型在20头奶牛中每头奶牛只有5个样本，准确率达到90.43%，优于其他最先进的方法。结果表明，该方法具有广泛的适用性和重要的应用价值。

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引用次数: 0

Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves 用于跨作物植物病害检测的创新型深度学习方法：识别不健康叶片的通用方法

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY

Information Processing in Agriculture

Pub Date : 2025-03-01 Epub Date: 2024-03-02 DOI: 10.1016/j.inpa.2024.03.002

Imane Bouacida , Brahim Farou , Lynda Djakhdjakha , Hamid Seridi , Muhammet Kurulay

One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions. These diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages, underscoring the urgency of implementing robust strategies to safeguard the world’s food sources. Deep learning methods have revolutionized the field of plant disease detection, offering advanced and accurate solutions for early identification and management. However, a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset. In this paper, we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops, even if the system was not trained on them. The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf, along with determining the disease’s prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of the Inception model in disease recognition, we employ a small Inception model architecture, which is suitable for processing small regions without compromising performance. To confirm the effectiveness of our method, we trained and tested it using the widely acclaimed PlantVillage dataset, recognized as the most utilized dataset for its comprehensive and diverse coverage. Our method achieved an accuracy rate of 94.04%. Furthermore, when tested on new datasets, it achieved an accuracy rate of 97.13%. This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases. In addition, it outperformed the existing methods in its ability to identify any disease across any crop type, showcasing its potential for broad applicability and contribution to global food security initiatives.

全球粮食安全面临的最严重威胁之一是危害农业生产力并威胁数百万人生计的植物病害。这些疾病可摧毁作物，扰乱粮食供应链，并加剧粮食短缺的风险，因此迫切需要实施强有力的战略，保护世界粮食来源。深度学习方法彻底改变了植物病害检测领域，为早期识别和管理提供了先进而准确的解决方案。然而，深度学习模型中一个反复出现的问题是，当面对未包含在训练数据集中的新作物和疾病类型时，它们容易缺乏鲁棒性和泛化。在本文中，我们通过提出一种新的基于深度学习的系统来解决这个问题，该系统能够识别不同作物的病叶和健康叶，即使系统没有对它们进行训练。关键思想是重点识别患病的小叶片区域，而不是患病叶片的整体外观，同时确定疾病在整个叶片上的患病率。为了有效的分类和利用Inception模型在疾病识别中的优点，我们采用了一个小的Inception模型架构，它适合处理小区域而不影响性能。为了确认我们方法的有效性，我们使用广受好评的PlantVillage数据集进行训练和测试，该数据集因其全面和多样化的覆盖范围而被公认为是最常用的数据集。该方法的准确率为94.04%。在新的数据集上进行测试，准确率达到97.13%。这种创新的方法不仅提高了植物病害检测的准确性，而且解决了模型泛化到不同作物和病害的关键挑战。此外，它在识别任何作物类型的任何疾病方面的能力优于现有方法，显示了其广泛适用性和对全球粮食安全倡议作出贡献的潜力。

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引用次数: 0