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 impro...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524001527 |
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| author | Yaqoob Majeed Mike O. Ojo Azlan Zahid |
| author_facet | Yaqoob Majeed Mike O. Ojo Azlan Zahid |
| author_sort | Yaqoob Majeed |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fe5bf8a4eaee4f2089aa52ef0b351c49 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-fe5bf8a4eaee4f2089aa52ef0b351c492025-08-20T01:59:34ZengElsevierSmart Agricultural Technology2772-37552024-12-01910054710.1016/j.atech.2024.100547Standalone edge AI-based solution for Tomato diseases detectionYaqoob Majeed0Mike O. Ojo1Azlan Zahid2Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA; Department of Electrical Engineering and Computer Science, University of Wyoming, Laramie, WY 82071, USADepartment of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USADepartment of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2772375524001527CNN, Deep learningEdge devicesDisease scoutingPlant diseaseEdge-AI |
| spellingShingle | Yaqoob Majeed Mike O. Ojo Azlan Zahid Standalone edge AI-based solution for Tomato diseases detection Smart Agricultural Technology CNN, Deep learning Edge devices Disease scouting Plant disease Edge-AI |
| title | Standalone edge AI-based solution for Tomato diseases detection |
| title_full | Standalone edge AI-based solution for Tomato diseases detection |
| title_fullStr | Standalone edge AI-based solution for Tomato diseases detection |
| title_full_unstemmed | Standalone edge AI-based solution for Tomato diseases detection |
| title_short | Standalone edge AI-based solution for Tomato diseases detection |
| title_sort | standalone edge ai based solution for tomato diseases detection |
| topic | CNN, Deep learning Edge devices Disease scouting Plant disease Edge-AI |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524001527 |
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