Classification and Grading of Harvested Mangoes Using Convolutional Neural Network

Iqbal, Hafiz Muhammad Rizwan; Hakim, Ayesha

doi:10.1080/15538362.2021.2023069

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…Momeny et al [120] improved CNN to detect and grade the appearance of cherry fruit and enhanced the accuracy of the algorithm. Iqbal et al [121] automatically classified eight mango varieties based on VGG16, ResNet152, and Inception v3. A new multi-view spatial network was developed to address apple grading challenges [122].…”

Section: Harvests Screening and Gradingmentioning

confidence: 99%

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

et al. 2023

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The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

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Section: Harvests Screening and Gradingmentioning

confidence: 99%

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

et al. 2023

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“…The CNN [ 33–35 ] is usually employed as the typical supervised learning classifier. As shown in Figure 1, the CNN classifier in this research consists of several layers, including the input layer, the hidden layer, and the output layer, where the hidden layer involves a convolutional layer, a pooling layer, and a fully connected layer, characterized as follows. The input layer: processing multi‐dimensional data. The convolutional layer: extracting features of input data. The pooling layer: performing feature selections. The fully connected layer: non‐linearly combining the extracted features to achieve the output, that is, using the existing high‐order features to achieve the learning goal. The softmax layer: normalizing output. The output layer: using logical or softmax functions to output classification labels. …”

Section: Preliminariesmentioning

confidence: 99%

“…The CNN [33][34][35] is usually employed as the typical supervised learning classifier. As shown in Figure 1, the CNN classifier in this research consists of several layers, including the input layer, the hidden layer, and the output layer, where the hidden layer involves a convolutional layer, a pooling layer, and a fully connected layer, characterized as follows.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A CNN approach based on correlation metrics to chemical process fault classifications with limited labelled data

Yin

2023

Can J Chem Eng

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This paper proposes a novel correlation metrics‐based convolutional neural network (CNN) classification model for chemical process fault diagnoses, creating a heuristic representation concerning process variable locations in grey correlation space (GCS) in terms of the copula entropy to guide the learning of classifiers. The proposed method based on correlation metrics can help solve the problem of insufficient information caused by a lack of labelled data. Specifically, variable correlations are fused into a heuristic matrix to provide prior knowledge for network learning in compensating data information before the CNN is employed to build the classifier for mining features in GCS. Driven by this mechanism, fault classifications in the case of small numbers of fault samples are successfully implemented. With successful simulation experiments carried out on the Tennessee Eastman (TE) process platform, we found that in GCS, different fault samples can represent hugely different features, while data resulting from the same fault rarely contribute to different ones. This observation lays a solid foundation for constructing superior fault classifiers. In addition, compared with conventional approaches, the proposed method has demonstrated better fault classification performances in the case of limited labelled fault samples.

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“…As deep learning network has been widely applied to crop target detection ( Fu et al., 2022 ), researchers began to use deep learning networks to solve crop detection problems in complex environments. For example, Iqbal and Hakim (2022) proposed a deep learning-based method for automatic classification and grading of eight harvested mango varieties using Inception v3, considering features such as color, size, shape, and texture. The proposed approach achieved up to 99.2% classification accuracy and 96.7% grading accuracy.…”

Section: Introductionmentioning

confidence: 99%

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

et al. 2023

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IntroductionReal-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process.MethodsTo reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments.ResultsThe experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops.DiscussionThe results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.

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Classification and Grading of Harvested Mangoes Using Convolutional Neural Network

Cited by 31 publications

References 16 publications

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

A CNN approach based on correlation metrics to chemical process fault classifications with limited labelled data

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm

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