FPGA–accelerated CNN for real-time plant disease identification

Luo, Yuexuan; Cheng, Xiang; Qi, Jiandong; Guo, Dan; Che, Wenqing

doi:10.1016/j.compag.2023.107715

Cited by 22 publications

(6 citation statements)

References 22 publications

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“…However, this approach could only determine whether a pixel belonged to a citrus or not, and could not classify the entire citrus as defective or not. Luo et al [32] proposed a 7-layer simple-structured network for real-time plant disease classification. By compressing the network and implementing it in FPGA, it achieved a processing delay of 0.071 s per frame.…”

Section: Fruit and Vegetable Defect Detectionmentioning

confidence: 99%

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Global to multi‐scale local architecture with hardwired CNN for 1‐ms tomato defect detection

Li,

Hu,

Fuchikami

et al. 2024

IET Image Processing

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A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps at 80.18%, only 1.31% lower than ResNet50 based detection system.

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Section: Fruit and Vegetable Defect Detectionmentioning

confidence: 99%

“…Luo et al. [32] proposed a 7‐layer simple‐structured network for real‐time plant disease classification. By compressing the network and implementing it in FPGA, it achieved a processing delay of 0.071 s per frame.…”

Section: Related Workmentioning

confidence: 99%

Global to multi‐scale local architecture with hardwired CNN for 1‐ms tomato defect detection

Li,

Hu,

Fuchikami

et al. 2024

IET Image Processing

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“…Sensors 2024, 24, x FOR PEER REVIEW 3 of 17 parallel executions of multi-tasks, the reusability of hardware, and reallocation of memory and computing resources [27][28][29]. The developed system helps to promote the on-theground application of optical sensing technology in the meat industry.…”

Section: Fluorescence Image Acquisition Devicementioning

confidence: 99%

“…In this study, inspired by the work of Wu et al [ 23 ] and developments in deep learning, a low-cost portable fluorescence imaging device for fish freshness detection is developed based on a field-programmable gate arrays (FPGA) board. The developed device utilizes a low-cost CMOS camera and an FPGA master unit to achieve effective acquisition of fluorescence images of fish samples and deploys a deep learning model (namely, YOLOv4-Tiny) for fish freshness detection by exploiting the advantages of FPGA, such as parallel executions of multi-tasks, the reusability of hardware, and reallocation of memory and computing resources [ 27 , 28 , 29 ]. The developed system helps to promote the on-the-ground application of optical sensing technology in the meat industry.…”

Section: Introductionmentioning

confidence: 99%

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Pan,

Huang,

Zhu

et al. 2024

Sensors

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Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

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“…To address this limitation, this study proposes combining target detection algorithms with disease diagnosis applications to identify the type of disease and locate the affected areas. By using deep learning-based plant disease detection methods [16], the diagnosis time in large production areas is reduced, and losses caused by manual diagnosis errors are minimized [17]. There are two main types of target detection methods: two-stage and one-stage methods [18].…”

Section: Introductionmentioning

confidence: 99%

A high-precision jujube disease spot detection based on SSD during the sorting process

Yin,

Liu,

Geng

et al. 2024

PLoS ONE

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The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 (mAP@0.5) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production.

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FPGA–accelerated CNN for real-time plant disease identification

Cited by 22 publications

References 22 publications

Global to multi‐scale local architecture with hardwired CNN for 1‐ms tomato defect detection

Global to multi‐scale local architecture with hardwired CNN for 1‐ms tomato defect detection

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

A high-precision jujube disease spot detection based on SSD during the sorting process

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