Surface Defect Detection of Fresh-Cut Cauliflowers Based on Convolutional Neural Network with Transfer Learning

Li, Yaodi; Xue, Jianxin; Wang, Kai; Zhang, Mingyue; Li, Zezhen

doi:10.3390/foods11182915

Cited by 10 publications

(6 citation statements)

References 49 publications

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“… Refs. Dataset Techniques Accuracy (%) 11 Sample images of cauliflower taken from Bangladesh Random forest 81.68 13 2500 images of cauliflower Inception V3 93.93 14 776 images of cauliflower Random Forest 89 16 Images of Xuebai MobileNetV1 95.63 17 Images of Eggplant diseases DenseNet201 99.06 Our Study VegNet EfficientNetB1 99.90 …”

Section: Analyzing the Resultsmentioning

confidence: 99%

“…Additionally, it demonstrated a highly impressive 99% accuracy in identifying cauliflower specimens afflicted by bacterial soft rot. In paper 16 , the researchers proposed a convolutional neural network (CNN) with transfer learning for the detection and classification of surface defects in fresh-cut cauliflower, aiming to overcome the inefficiencies of manual detection methods. The dataset comprises 4,790 cauliflower images categorized as diseased, healthy, mildewed, and browning.…”

Section: Introductionmentioning

confidence: 99%

“… Dataset No. of images Techniques Outcomes Limitation/remarks 11 Cauliflower image InceptionV3 Accuracy = 90.08% The authors stated that InceptionV3 accurately and effectively recognize diseases in cauliflower plants Random Search Accuracy = 81.68% 2 VegNet dataset 656 original images of cauliflower – – This dataset is suitable for developing robust applications focused on the accurate classification and detection of cauliflower-related issues 13 – 2500 images of cauliflower InceptionV3 Accuracy = 93.93% The authors suggested that leveraging advanced CNN model improve the automation of plant disease detection and classification 14 – 776 images of cauliflower Random forest Accuracy = 89% Small dataset 15 655 cauliflower images 2D-LeNet Accuracy = 99% Small dataset 16 Xuebai dataset 4790 images of cauliflower MobileNetV2 Accuracy = 99.27% Loss = 0.033 F1 score = 99.24% Limited tested dataset 17 – Raw images of eggplant DenseNet201 Accuracy = 99.06% Precision = 99.45% Specificity = 99.49% Sensitivity = 87.30% …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced deep learning techniques for early disease prediction in cauliflower plants

Kanna,

Kumar,

Kumar

et al. 2023

Sci Rep

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Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers’ incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers.

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Section: Analyzing the Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced deep learning techniques for early disease prediction in cauliflower plants

Kanna,

Kumar,

Kumar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Because previous studies have shown that these random features perform much better on standard transfer tasks than on random levels [ 44 ]. After the feature extraction, the extracted features (4096 dimensional) of Camellia oleifera fruit images are output through the penultimate fully connected layer of the VGG16-D FC model [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Zhu

Shen

Wang

et al. 2022

Foods

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Maturity grading and identification of Camellia oleifera are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in Camellia oleifera production and research is the worldwide confusion regarding the grading and identification of Camellia oleifera fruit maturity. To solve this problem, a Camellia oleifera fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 Camellia oleifera fruit samples and 2628 Camellia oleifera fruit digital images collected using a smartphone. The proposed model for grading Camellia oleifera fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 Camellia oleifera fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for Camellia oleifera fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for Camellia oleifera fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of Camellia oleifera fruits, which is especially critical for the improvement of Camellia oil production and quality.

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“…Among these, InceptionV3 exhibited the highest accuracy at 90.08%, showcasing superior performance compared to the traditional machine learning approach. ( Li et al., 2022 ) introduced a detection and classification model for surface defects in fresh-cut cauliflower based on a CNN with transfer learning. A dataset comprising 4,790 images of fresh-cut cauliflower, categorized into healthy, diseased, browning, and mildewed classes, was collected for the study.…”

Section: Literature Reviewmentioning

confidence: 99%

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Uddin,

Mazumder,

Prity

et al. 2024

Front. Plant Sci.

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Cauliflower cultivation plays a pivotal role in the Indian Subcontinent’s winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely ‘Bacterial Soft Rot’, ‘Downey Mildew’ and ‘Black Rot’ are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability

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Surface Defect Detection of Fresh-Cut Cauliflowers Based on Convolutional Neural Network with Transfer Learning

Cited by 10 publications

References 49 publications

Advanced deep learning techniques for early disease prediction in cauliflower plants

Advanced deep learning techniques for early disease prediction in cauliflower plants

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

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