{"title":"基于人工智能的番茄病害独立边缘检测解决方案","authors":"","doi":"10.1016/j.atech.2024.100547","DOIUrl":null,"url":null,"abstract":"<div><p>Tomato yield is significantly affected by diseases, which are a continuous challenge for its production and pose threats to its global supply chain. Automatic and early detection of these diseases could help growers to swiftly adopt mitigation strategies to limit the disease spread, leading to improved production. Deep learning-based CNN approaches have been widely applied to detect tomato diseases. However, deep learning models are highly computationally demanding, resulting in a computational bottleneck for practical adaptation for agricultural applications such as disease detection and monitoring. Over the last few years, developments of open-source Edge systems have provided opportunities for low-cost and low-power consumption practical solutions for deep learning applications for agriculture. Therefore, the primary goal of this study was to evaluate the performance of standalone Edge-AI solutions for tomato leaf disease detection. To achieve this goal, firstly, this study employed lightweight deep learning networks to detect and differentiate tomato leaf diseases (bacterial spot, early blight, healthy, late blight, leaf mold, septoria leaf spot, two spotted spider mites, target spot, and yellow leaf curl virus). Then, these deep learning networks were deployed on low-cost and low-power consumption Edge devices to investigate their performance capabilities as standalone Edge-AI solutions for the early detection of tomato leaf diseases. Lightweight CNN based GoogleNet and MobileNetV2 deep learning networks achieved accuracies of up to 98.25 % compared to accuracies of 98.13 %, 98.13 %, 94.62 %, and 90.67 % of EfficientNetB0, ResNet-18, SqueezeNet, and NasNetMobile, respectively, in detecting tomato diseases. NVIDIA Jetson ORIN AGX and Nano significantly outperformed Raspberry Pi and Raspberry Pi with AI accelerator (Google Coral) in image classification, achieving classification times of 3.5 ms and 5.2 ms respectively, using SqueezeNet, compared to 15.3 ms and 10.5 ms on the Raspberry Pi devices. In addition, Raspberry Pi with Google Coral achieved the best cost/FPS performance of 0.14 compared to other Edge devices NVIDIA Jetson AGX Orin and NVIDIA Jetson Nano Orin with cost/FPS of 0.7 and 0.26, respectively. These results showed the potential of standalone Edge-AI solutions using low-cost and low-power consuming software and hardware resources for early tomato disease detections.</p></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772375524001527/pdfft?md5=2c2f38fd03217d8bd2db2d3d1b6d0251&pid=1-s2.0-S2772375524001527-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Standalone edge AI-based solution for Tomato diseases detection\",\"authors\":\"\",\"doi\":\"10.1016/j.atech.2024.100547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Tomato yield is significantly affected by diseases, which are a continuous challenge for its production and pose threats to its global supply chain. Automatic and early detection of these diseases could help growers to swiftly adopt mitigation strategies to limit the disease spread, leading to improved production. Deep learning-based CNN approaches have been widely applied to detect tomato diseases. However, deep learning models are highly computationally demanding, resulting in a computational bottleneck for practical adaptation for agricultural applications such as disease detection and monitoring. Over the last few years, developments of open-source Edge systems have provided opportunities for low-cost and low-power consumption practical solutions for deep learning applications for agriculture. Therefore, the primary goal of this study was to evaluate the performance of standalone Edge-AI solutions for tomato leaf disease detection. To achieve this goal, firstly, this study employed lightweight deep learning networks to detect and differentiate tomato leaf diseases (bacterial spot, early blight, healthy, late blight, leaf mold, septoria leaf spot, two spotted spider mites, target spot, and yellow leaf curl virus). Then, these deep learning networks were deployed on low-cost and low-power consumption Edge devices to investigate their performance capabilities as standalone Edge-AI solutions for the early detection of tomato leaf diseases. Lightweight CNN based GoogleNet and MobileNetV2 deep learning networks achieved accuracies of up to 98.25 % compared to accuracies of 98.13 %, 98.13 %, 94.62 %, and 90.67 % of EfficientNetB0, ResNet-18, SqueezeNet, and NasNetMobile, respectively, in detecting tomato diseases. NVIDIA Jetson ORIN AGX and Nano significantly outperformed Raspberry Pi and Raspberry Pi with AI accelerator (Google Coral) in image classification, achieving classification times of 3.5 ms and 5.2 ms respectively, using SqueezeNet, compared to 15.3 ms and 10.5 ms on the Raspberry Pi devices. In addition, Raspberry Pi with Google Coral achieved the best cost/FPS performance of 0.14 compared to other Edge devices NVIDIA Jetson AGX Orin and NVIDIA Jetson Nano Orin with cost/FPS of 0.7 and 0.26, respectively. 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引用次数: 0
摘要
番茄产量受到病害的严重影响,这是番茄生产面临的一个持续挑战,并对其全球供应链构成威胁。自动和早期检测这些病害有助于种植者迅速采取缓解策略,限制病害蔓延,从而提高产量。基于深度学习的 CNN 方法已被广泛应用于检测番茄病害。然而,深度学习模型对计算要求很高,导致在疾病检测和监测等农业应用的实际应用中遇到计算瓶颈。过去几年,开源 Edge 系统的发展为农业深度学习应用提供了低成本、低功耗的实用解决方案。因此,本研究的主要目标是评估独立边缘人工智能解决方案在番茄叶片疾病检测方面的性能。为实现这一目标,首先,本研究采用轻量级深度学习网络来检测和区分番茄叶片病害(细菌斑病、早疫病、健康病、晚疫病、叶霉病、败酱病叶斑、双斑蜘蛛螨、靶斑病和黄叶卷曲病毒)。然后,将这些深度学习网络部署到低成本、低功耗的边缘设备上,研究它们作为独立边缘人工智能解决方案在早期检测番茄叶片病害方面的性能。在检测番茄病害方面,基于轻量级 CNN 的 GoogleNet 和 MobileNetV2 深度学习网络的准确率高达 98.25%,而 EfficientNetB0、ResNet-18、SqueezeNet 和 NasNetMobile 的准确率分别为 98.13%、98.13%、94.62% 和 90.67%。英伟达™(NVIDIA®)Jetson ORIN AGX和Nano在图像分类方面的表现明显优于Raspberry Pi和配备人工智能加速器(Google Coral)的Raspberry Pi,使用SqueezeNet实现的分类时间分别为3.5毫秒和5.2毫秒,而Raspberry Pi设备的分类时间分别为15.3毫秒和10.5毫秒。此外,装有 Google Coral 的 Raspberry Pi 实现了最佳成本/每秒 0.14 的性能,而其他 Edge 设备 NVIDIA Jetson AGX Orin 和 NVIDIA Jetson Nano Orin 的成本/每秒分别为 0.7 和 0.26。这些结果表明,利用低成本、低功耗的软件和硬件资源,独立的边缘人工智能解决方案在早期番茄疾病检测方面具有巨大潜力。
Standalone edge AI-based solution for Tomato diseases detection
Tomato yield is significantly affected by diseases, which are a continuous challenge for its production and pose threats to its global supply chain. Automatic and early detection of these diseases could help growers to swiftly adopt mitigation strategies to limit the disease spread, leading to improved production. Deep learning-based CNN approaches have been widely applied to detect tomato diseases. However, deep learning models are highly computationally demanding, resulting in a computational bottleneck for practical adaptation for agricultural applications such as disease detection and monitoring. Over the last few years, developments of open-source Edge systems have provided opportunities for low-cost and low-power consumption practical solutions for deep learning applications for agriculture. Therefore, the primary goal of this study was to evaluate the performance of standalone Edge-AI solutions for tomato leaf disease detection. To achieve this goal, firstly, this study employed lightweight deep learning networks to detect and differentiate tomato leaf diseases (bacterial spot, early blight, healthy, late blight, leaf mold, septoria leaf spot, two spotted spider mites, target spot, and yellow leaf curl virus). Then, these deep learning networks were deployed on low-cost and low-power consumption Edge devices to investigate their performance capabilities as standalone Edge-AI solutions for the early detection of tomato leaf diseases. Lightweight CNN based GoogleNet and MobileNetV2 deep learning networks achieved accuracies of up to 98.25 % compared to accuracies of 98.13 %, 98.13 %, 94.62 %, and 90.67 % of EfficientNetB0, ResNet-18, SqueezeNet, and NasNetMobile, respectively, in detecting tomato diseases. NVIDIA Jetson ORIN AGX and Nano significantly outperformed Raspberry Pi and Raspberry Pi with AI accelerator (Google Coral) in image classification, achieving classification times of 3.5 ms and 5.2 ms respectively, using SqueezeNet, compared to 15.3 ms and 10.5 ms on the Raspberry Pi devices. In addition, Raspberry Pi with Google Coral achieved the best cost/FPS performance of 0.14 compared to other Edge devices NVIDIA Jetson AGX Orin and NVIDIA Jetson Nano Orin with cost/FPS of 0.7 and 0.26, respectively. These results showed the potential of standalone Edge-AI solutions using low-cost and low-power consuming software and hardware resources for early tomato disease detections.