Intelligent grading method for walnut kernels based on deep learning and physiological indicators

Chen, Siwei; Dai, Dan; Jian, Zheng; Kang, Haoyu; Wang, Dongdong; Zheng, Xinyu; Gu, Xiaosong; Mo, Jiali; Luo, Zhihan

doi:10.3389/fnut.2022.1075781

Cited by 8 publications

(2 citation statements)

References 24 publications

(29 reference statements)

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“…ResNet152V2 has already shown its potential to transform conventional farming by being used for tasks such as grading walnut kernels [39] and weed detection. Biometric identification of Black Bengal goats [40] demonstrates the potential of ResNet152V2 for wildlife conservation through its application to animal identification.…”

Section: A Resnet152v2mentioning

confidence: 99%

WeedFocusNet: A Revolutionary Approach using the Attention-Driven ResNet152V2 Transfer Learning

Devi,

Sandhya,

Chatrapati

2023

IJRITCC

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The advancement of modern agriculture is heavily dependent on accurate weed detection, which contributes to efficient resource utilization and increased crop yield. Traditional methods, however, often need more accuracy and efficiency. This paper presents WeedFocusNet, an innovative approach that leverages attention-driven ResNet152V2 transfer learning addresses these challenges. This approach enhances model generalization and focuses on critical features for weed identification, thereby overcoming the limitations of existing methods. The objective is to develop a model that enhances weed detection accuracy and optimizes computational efficiency. WeedFocusNet, a novel deep-learning model, performs weed detection better by employing attention-driven transfer learning based on the ResNet152V2 architecture. The model integrates an attention module, concentrating its predictions on the most significant image features. Evaluated on a dataset of weed and crop images, WeedFocusNet achieved an accuracy of 99.28%, significantly outperforming previous methods and models, such as MobileNetV2, ResNet50, and custom CNN models, in terms of accuracy, time complexity, and memory usage, despite its larger memory footprint. These results emphasize the transformative potential of WeedFocusNet as a powerful approach for automating weed detection in agricultural fields.

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Section: A Resnet152v2mentioning

confidence: 99%

WeedFocusNet: A Revolutionary Approach using the Attention-Driven ResNet152V2 Transfer Learning

Devi,

Sandhya,

Chatrapati

2023

IJRITCC

View full text Add to dashboard Cite

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“…The method was tested on a total of 6,068 tobacco images from 7 grades and the final grading accuracy was 80.14%. Chen et al ( 19 ) proposed an intelligent grading method for pecan kernels based on deep learning and physiological indicators, with a dataset of 4,395 images of pecan kernels of four grades enhanced to 6,213 images and a final grading accuracy of 92.2% on the test set.…”

Section: Introductionmentioning

confidence: 99%

Dried shiitake mushroom grade recognition using D-VGG network and machine vision

Wang,

Dong,

Wang

et al. 2023

Front. Nutr.

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Grading dried shiitake mushrooms is an indispensable production step, as there are large quality differences between different grades, which affect the product’s price and marketability. Dried shiitake mushroom samples have irregular shapes, small morphological differences between different grades of the same species, and they may occur in mixed grades, which causes challenges to the automatic grade recognition using machine vision. In this study, a comprehensive method to solve this problem is provided, including image acquisition, preprocessing, dataset creation, and grade recognition. The osprey optimization algorithm (OOA) is used to improve the computational efficiency of Otsu’s threshold binarization and obtain complete mushroom contours samples efficiently. Then, a method for dried shiitake mushroom grade recognition based on the improved VGG network (D-VGG) is proposed. The method uses the VGG16 network as the base framework, optimizes the convolutional layer of the network, and uses a global average pooling layer instead of a fully connected layer to reduce the risk of model overfitting. In addition, a residual module and batch normalization are introduced to enhance the learning effect of texture details, accelerate the convergence of the model, and improve the stability of the training process. An improved channel attention network is proposed to enhance the feature weights of different channels and improve the grading performance of the model. The experimental results show that the improved network model (D-VGG) can recognize different dried shiitake mushroom grades with high accuracy and recognition efficiency, achieving a final grading accuracy of 96.21%, with only 46.77 ms required to process a single image. The dried shiitake mushroom grade recognition method proposed in this study provides a new implementation approach for the dried shiitake mushroom quality grading process, as well as a reference for real-time grade recognition of other agricultural products.

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Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics

Caliskan,

Kartal,

Balekoglu

et al. 2024

Eur J Forest Res

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The phenotype of a woody plant represents its unique morphological properties. Population discrimination and individual classification are crucial for breeding populations and conserving genetic diversity. Machine Learning (ML) algorithms are gaining traction as powerful tools for predicting phenotypes. The present study is focused on classifying and clustering the seeds and seedlings in terms of morphological characteristics using ML algorithms. In addition, the k-means algorithm is used to determine the ideal number of clusters. The results obtained from the k-means algorithm were then compared with reality. The best classification performance achieved by the Random Forest algorithm was an accuracy of 0.648 and an F1-Score of 0.658 for the seed traits. Also, the best classification performance for stone pine seedlings was observed for the k-Nearest Neighbors algorithm (k = 18), for which the accuracy and F1-Score were 0.571 and 0.582, respectively. The best clustering performance was achieved with k = 2 for the seed (average Silhouette index = 0.48) and seedling (average Silhouette Index = 0.51) traits. According to the principal component analysis, two dimensions accounted for 97% and 63% of the traits of seeds and seedlings, respectively. The most important features between the seed and seedling traits were cone weight and bud set, respectively. This study will provide a foundation and motivation for future efforts in forest management practices, particularly regarding reforestation, yield optimization, and breeding programs.

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Intelligent grading method for walnut kernels based on deep learning and physiological indicators

Cited by 8 publications

References 24 publications

WeedFocusNet: A Revolutionary Approach using the Attention-Driven ResNet152V2 Transfer Learning

WeedFocusNet: A Revolutionary Approach using the Attention-Driven ResNet152V2 Transfer Learning

Dried shiitake mushroom grade recognition using D-VGG network and machine vision

Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics

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