Defect Classification of Green Plums Based on Deep Learning

Zhou, Hu; Zhuang, Zilong; Liu, Ying; Liu, Yang; Zhang, Xiao

doi:10.3390/s20236993

Cited by 38 publications

(19 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, ref. [14] used an improved VGG network to detect and classify green plum defects into rot, cracks, rain spots, scars, and intact skin. Compared to our study, their developed VGG model achieved lower recall, precision, and F1-score of 78, 93 and 85% for crack detection in green plums under controlled imaging conditions.…”

Section: Resultsmentioning

confidence: 99%

“…Their results showed that the R-FCN ResNet101 had the best overall performance in detection speed and accuracy [13]. Likewise, a CNN and a computer vision method to classify defects and damages of green plums was adopted [14]. The developed CNN model was based on the VGG network architecture combined with a stochastic weight averaging optimizer and trained weights on ImageNet.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

2021

View full text Add to dashboard Cite

Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

2021

View full text Add to dashboard Cite

show abstract

“…There has been an increasing growth of research focusing on plant disease classification in recent years aiming to develop effective plant diagnostics systems for farmers [ 21 , 22 , 23 ]. A variety of Artificial Intelligence (AI) methods have been adopted in classifying and detection various plant diseases such as olive [ 24 ], pomegranate [ 25 ], plum [ 26 ], rice [ 27 ], tomato [ 28 ], cassava [ 29 ], mango [ 30 ], tea leaf [ 31 ], apple [ 32 ], citrus [ 33 ], oranges [ 34 ], etc.…”

Section: Related Workmentioning

confidence: 99%

AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Almadhor

Rauf

Lali

et al. 2021

Sensors

View full text Add to dashboard Cite

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

show abstract

“…To avoid the above problems and forgo the dependence on artificial feature extraction, increasingly, many deep learning methods (e.g., deep belief network (DBN) [ 8 ], convolutional neural network (CNN) [ 9 ], auto-encoder (AE) [ 10 ] and stacked denoising auto-encoder (SDAE) [ 11 ]) are being presented to autonomously mine the representative diagnostic information hidden in the raw data and have received great attention in intelligent fault diagnosis. Shao et al [ 12 ] presented a novel method, called an adaptive deep belief network (DBN), with a dual-tree complex wavelet packet (DTCWPT) to process bearing vibration signals and achieve fault diagnosis of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Yan

She

et al. 2021

Entropy

View full text Add to dashboard Cite

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

show abstract

Defect Classification of Green Plums Based on Deep Learning

Cited by 38 publications

References 33 publications

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Contact Info

Product

Resources

About