Research on Multi-class Fruits Recognition Based on Machine Vision and SVM

Peng, Hongxing; Shao, Yuanyuan; Chen, Keying; Deng, Yihai; Xue, Chao

doi:10.1016/j.ifacol.2018.08.094

Cited by 20 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, researchers have also applied machine learning [2] technology to the field of fruit classification for fruit image recognition and classification.Peng H [3] et al use machine learning technology to classify fruit images, and apply the extracted feature vectors to SVM classifiers to classify and recognize fruit images. Deep learning [4] is the development of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Fruit image classification based on ResNet50 and transfer learning

Lin,

Yu,

Wan

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

View full text Add to dashboard Cite

Developing the cash crop industry and helping rural revitalization is China's long-term development plan, applying image recognition technology to the field of cash crops, and efficiently realizing the picking and classification of crops. In this paper, three transfer learning methods are used to compare the transfer learning effects of AlexNet, VGG16, ResNet50 and other networks to select the most suitable model for fruit classification. ResNet50 migration training fully connected models can not only shorten the training time, but also achieve higher accuracy under the same iteration cycle. The accuracy of ResNet50 migration training fully connected layer model is 97%. The accuracy rate is improved by17%compared to VGG13.

show abstract

Section: Introductionmentioning

confidence: 99%

Fruit image classification based on ResNet50 and transfer learning

Lin,

Yu,

Wan

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

View full text Add to dashboard Cite

show abstract

“…Currently, there has been some progress in the research of fruit target detection in natural scenes. Peng et al [ 1 ] proposed to use shape moment invariants and other methods to integrate fruit color and shape features, and use SVM classifier to classify and recognize the extracted feature vectors. However, this method has poor adaptability in complex environment.…”

Section: Introductionmentioning

confidence: 99%

YOLO-plum: A high precision and real-time improved algorithm for plum recognition

Niu,

Lu,

Liang

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant challenges to accurate and complete labeling using mainstream algorithms like YOLOv5. In this study, we established the first artificial dataset of plums and used deep learning to improve target detection. Our improved YOLOv5 algorithm achieved more accurate and rapid batch identification of immature plums, resulting in improved quality and economic benefits. The YOLOv5-plum algorithm showed 91.65% recognition accuracy for immature plums after our algorithmic improvements. Currently, the YOLOv5-plum algorithm has demonstrated significant advantages in detecting unripe plums and can potentially be applied to other unripe fruits in the future.

show abstract

“…As the core field of artificial intelligence (Nyarko et al, 2018 ; Peng et al, 2018 ; Liu Q. et al, 2019 ; Jia et al, 2020 ), traditional machine learning has milestone significance for the development of image recognition. Machine learning methods first need to extract relevant features from many datasets, then learn the data through algorithms, and finally make prediction and decision, which is the key to the realization of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Accurate segmentation of green fruit based on optimized mask RCNN application in complex orchard

et al. 2022

View full text Add to dashboard Cite

Fruit and vegetable picking robots are affected by the complex orchard environment, resulting in poor recognition and segmentation of target fruits by the vision system. The orchard environment is complex and changeable. For example, the change of light intensity will lead to the unclear surface characteristics of the target fruit; the target fruits are easy to overlap with each other and blocked by branches and leaves, which makes the shape of the fruits incomplete and difficult to accurately identify and segment one by one. Aiming at various difficulties in complex orchard environment, a two-stage instance segmentation method based on the optimized mask region convolutional neural network (mask RCNN) was proposed. The new model proposed to apply the lightweight backbone network MobileNetv3, which not only speeds up the model but also greatly improves the accuracy of the model and meets the storage resource requirements of the mobile robot. To further improve the segmentation quality of the model, the boundary patch refinement (BPR) post-processing module is added to the new model to optimize the rough mask boundaries of the model output to reduce the error pixels. The new model has a high-precision recognition rate and an efficient segmentation strategy, which improves the robustness and stability of the model. This study validates the effect of the new model using the persimmon dataset. The optimized mask RCNN achieved mean average precision (mAP) and mean average recall (mAR) of 76.3 and 81.1%, respectively, which are 3.1 and 3.7% improvement over the baseline mask RCNN, respectively. The new model is experimentally proven to bring higher accuracy and segmentation quality and can be widely deployed in smart agriculture.

show abstract

Research on Multi-class Fruits Recognition Based on Machine Vision and SVM

Cited by 20 publications

References 2 publications

Fruit image classification based on ResNet50 and transfer learning

Fruit image classification based on ResNet50 and transfer learning

YOLO-plum: A high precision and real-time improved algorithm for plum recognition

Accurate segmentation of green fruit based on optimized mask RCNN application in complex orchard

Contact Info

Product

Resources

About