A Deep Learning Based Approach for Strawberry Yield Prediction via Semantic Graphics

Ilyas, Talha; Kim, Hyongsuk

doi:10.23919/iccas52745.2021.9649871

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…With the development of AI, sensor technology, and computer vision technology, research has been conducted to apply these technologies to the agricultural field, such as smart farms, to create high added-value [28][29][30][31][32][33][34][35]. In particular, for the system that determines the ripeness of fruits, many studies have been carried out utilizing AI and computer vision technology, as it is often necessary to judge the ripeness by external factors such as color, shape, etc.…”

Section: Related Workmentioning

confidence: 99%

“…Strawberries have been widely employed in many studies as a fruit for ripeness detection research based on computer vision technology due to their distinct color changes depending on the degree of ripeness [31][32][33][34][35]. The authors in [31] utilized the YOLOv7 model for object detection and ripeness measurement of strawberries.…”

Section: Related Workmentioning

confidence: 99%

“…The maximum accuracy of the system was 85.64%. In [35], a ripeness detection system that combines semantic graphics for data annotation with a fully convolutional neural network (FCN) made up of an encoder and an expanding decoder was proposed, showing an average ripeness prediction accuracy of 88.57%. Table 1 displays the comparison of related works regarding strawberry ripeness estimation mentioned above.…”

Section: Related Workmentioning

confidence: 99%

“…Through this approach, we achieved high performance with a relatively small memory resource of 128 bytes, showcasing a precision of 93.4%, recall of 93.3%, and an F1 score of 0.933. 4 presents the classification accuracy, parameter number for operations, and execution time for strawberry ripeness in several similar studies [31][32][33][34][35], as well as representative lightweight algorithms such as k-NN and MobileNet, and our system. The number of parameters used in operations is closely related to the computational capabilities of each system.…”

Section: Comparison According To Model Memory Sizementioning

confidence: 99%

See 3 more Smart Citations

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Park,

Shin,

Kim

et al. 2024

Electronics

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Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment. In this paper, we propose a strawberry ripeness classification system utilizing a convolution-based feature extractor (CoFEx) for accelerating convolution operations and an edge AI processor, Intellino, for replacing FC layer operations. We accelerated feature map extraction utilizing the CoFEx constructed with systolic array (SA) and alleviated the computational burden and memory overhead associated with the FC layer operations by replacing them with the k-nearest neighbors (k-NN) algorithm. The CoFEx and the Intellino both were designed with Verilog HDL and implemented on a field-programmable gate array (FPGA). The proposed system achieved a high precision of 93.4%, recall of 93.3%, and F1 score of 0.933. Therefore, we demonstrated a feasibility of the strawberry ripeness classification system operating in an embedded environment.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison According To Model Memory Sizementioning

confidence: 99%

See 2 more Smart Citations

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Park,

Shin,

Kim

et al. 2024

Electronics

View full text Add to dashboard Cite

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“…The importance of strawberry disease detection in agriculture cannot be overstated. Strawberry plants are susceptible to a range of diseases, including powdery mildew, gray mold, and anthracnose, among others [5], [6]. These diseases can lead to significant reductions in crop yield, rendering them a major concern for strawberry growers.…”

Section: Introductionmentioning

confidence: 99%

Advancing Strawberry Disease Detection in Agriculture: A Transfer Learning Approach with YOLOv5 Algorithm

LIU

2024

IJACSA

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Strawberry Disease Detection in the Agricultural Sector is of paramount importance, as it directly impacts crop yield and quality. A multitude of methods have been explored in the literature to address this challenge, but deep learning techniques have consistently demonstrated superior accuracy in disease detection. Nevertheless, the current research challenge in deep learning-based strawberry disease detection remains the demand for consistently high accuracy rates. In this study, we propose a deep learning model based on the Yolov5 architecture to address the aforementioned research challenge effectively. Our approach involves the generation of a custom dataset tailored to strawberry disease detection and the execution of comprehensive training, validation, and testing processes to fine-tune the model. Experimental results and performance evaluations were conducted to validate our proposed method, demonstrating its ability to achieve accurate results consistently. This research contributes to the ongoing efforts to enhance strawberry disease detection methods within the agricultural sector, ultimately aiding in the early identification and mitigation of diseases to preserve crop yield and quality.

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Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision

Yang,

Liu,

Zhou

et al. 2024

Computers and Electronics in Agriculture

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A Deep Learning Based Approach for Strawberry Yield Prediction via Semantic Graphics

Cited by 7 publications

References 24 publications

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor

Advancing Strawberry Disease Detection in Agriculture: A Transfer Learning Approach with YOLOv5 Algorithm

Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision

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