A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

Jin, Xin; Wang, Chenglin; Chen, Kaikang; Ji, Jiangtao; Liu, Suchwen; Wang, Yawei

doi:10.3389/fpls.2021.691753

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…In contrast, the authors in [55] employ an approach similar to Otsu, but they do it using the Fiji-ImageJ programme to differentiate the portions of the leaf image that are bright green and sections that are dark green. In the research by [56], the authors segment an image of healthy potted leaf seedlings using an optimized thresholding method based on a genetic algorithm. The technique begins with converting the input grayscale image into a 16-bit binary number map.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Chili leaf Disease identification (R2 value ≤ 0.99) [55] Optimal thresholding Chili leaf Disease identification ≤ 94% [56] A k-means clustering approach is an unsupervised form of machine learning used to segment ROI, such as those derived from images of crop leaves. Since it is suited for data sets with enormous quantities of data and high feature dimensions, as well as having a low dependency on the data itself, the k-means clustering technique has become one of the most used approaches for segmentation [57].…”

Section: Image Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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Chili, an important crop whosefruit is used as a spice, is significantly hampered by the existence of chili diseases.While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 researchpaperpublications on theidentificationof chilidiseases, with publication dates spanning from 2013 to 2021, arereviewed in this paper.Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.

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Section: Image Segmentationmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

View full text Add to dashboard Cite

show abstract

“…By obtaining information on the vegetation statistics values of each cell, the method achieved a 100% accurate classification of plug cells and seedling cells. Jin et al (2021) proposed a computer vision-based architecture to identify seedlings accurately. The method extracts leaf area information from plug seedlings using a genetic algorithm and a three-dimensional block matching algorithm with optimal threshold segmentation.…”

Section: Introductionmentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

Self Cite

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The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.

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“…The CNN model algorithm was used to identify the single plant, multiple plants, and cavity trays, and the identification accuracy rate of vigorous seedlings can reach 99.05%. Jin et al (2021) proposed a threshold optimization method based on a genetic algorithm and a three-dimensional block matching algorithm (BM3D). The leaf area of potted seedlings was measured by machine vision technology, and the growth status and location information of potted seedlings were detected.…”

Section: Introductionmentioning

confidence: 99%

“…The method proposed by Jin et al (2021) was used to identify the healthy seedlings of lettuce seedlings, but it was not practical for identifying them. As shown in Figure 1, a comparative image of lettuce seedlings and pepper seedlings is shown.…”

Section: Introductionmentioning

confidence: 99%

Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network

Jin

Tang

et al. 2022

Front. Plant Sci.

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To solve the problem of low survival rate caused by unscreened transplanting of seedlings. This study proposed a selective transplanting method of leafy vegetable seedlings based on the ResNet 18 network. Lettuce seedlings were selected as the research object, and a total of 3,388 images were obtained in the dataset. The images were randomly divided into the training set, validation set, and test set in the ratio of 6:2:2. The ResNet 18 network was used to perform transfer learning after tuning, identifying, and classifying leafy vegetable seedlings, and then establishing a model to screen leafy vegetable seedlings. The results showed that the optimal detection accuracy of the presence and health of seedlings in the training data set was above 100%, and the model loss remained at around 0.005. Nine hundred seedlings were selected for the validation test, and the screening accuracy rate was 97.44%, the precision rate of healthy seedlings was 97.56%, the recall rate was 97.34%, the precision rate of unhealthy seedlings was 92%, and the recall rate was 92.62%, which was better than the screening model based on the physical characteristics of seedlings. If they were identified as unhealthy seedlings, the manipulator would remove them during the transplanting process and perform the seedling replenishment operation to increase the survival rate of the transplanted seedlings. Moreover, the seedling image is extracted by background removal technology, so the model processing time for a single image is only 0.0129 s. This research will provide technical support for the selective transplantation of leafy vegetable seedlings.

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A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

Cited by 6 publications

References 19 publications

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network

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