Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks

Ji, Miaomiao; Zhang, Keke; Wu, Qiufeng; Deng, Zhao

doi:10.1007/s00500-020-04866-z

Cited by 39 publications

(10 citation statements)

References 36 publications

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“…In recent years, with the rapid development of information technology, the rise of online video websites, and the rapid development of the film industry, the related research in the field of film has been greatly promoted [ 1 – 3 ]. At the same time, people's needs are becoming more and more personalized, so many program types can no longer be simply classified into a certain category but are often mixed and varied in various forms [ 4 – 6 ]. All-round information collection and analysis of programs can describe a program more accurately and completely, so that viewers can make choices more intuitively, and at the same time, publishers can have a more comprehensive understanding of programs, thus facilitating management and operation [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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“…In addition, human emotions and sentiments are sometimes regarded as a multi-label classification problem nowadays, e.g., multiple fine-grained emotions may coexist in a single tweet of a microblog [21]. In addition, multi-label classifiers have recently been utilized for recognizing crop diseases in agriculture [27]. The learning algorithms for these problems are the "multi-label classifiers" as reviewed in [47,58].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised multi-label classification using an extended graph-based manifold regularization

Ding

Dick

2022

Complex Intell. Syst.

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Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.

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“…For corn leaf disease recognition and classification (Waheed et al, 2020) [25] proposed an optimized dense convolutional neural network model. Ji et al (2020) [26] proposed a Convolutional Neural Network-based architecture for multi-label learning for crop leaf diseases recognition and severity estimation. To overcome the problem of the unbalanced dataset (Zhong and Zhao, 2020) [27] have proposed DenseNet-121 as the backbone network and used three methods regression, multi-label classification, and focus on loss function to identify apple life disease and obtained test accuracy of 93.51%, 93.31% and 93.71% respectively which was better than the accuracy of 92.29% obtained by traditional multi-classification method with a cross-entropy loss function.…”

Section: Introductionmentioning

confidence: 99%

Groundnuts Leaf Disease Recognition using Neural Network with Progressive Resizing

Rakholia¹,

Tailor²,

Saini³

et al. 2022

IJACSA

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Groundnut is an important oilseed crop in the world, and India is the second-largest producer of groundnuts. This crop is prone to attack by numerous diseases which is one of the most important factors contributing to the loss of productivity and degradation in the quality; both of these finally result in a low agricultural economy. Therefore, it is necessary to find better and more reliable automation solutions to recognize groundnut leaf diseases. In this paper, a deep learning based model with progressive resizing is proposed for groundnut leaf disease recognition and classification tasks. Five major categories of groundnut leaf diseases namely leaf spot, armyworms effect, wilts, yellow leaf, and healthy leaf are considered. The proposed model was trained with and without progressive resizing while it was validated using cross-entropy loss. The first of its kind dataset used for training and validation purposes was manually created from the Saurashtra region of Gujarat state of India. The created dataset was imbalanced in terms of a different number of samples for each category. To handle the imbalanced dataset problem, the extended focal loss function was used. To evaluate the performance of the proposed model, different performance measures including precision, sensitivity, F1-score, and accuracy were applied. The proposed model achieved state-of-the-art accuracy of 96.12%. The model with progressive resizing performed better than the traditional core neural network-based model built on cross-entropy loss.

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Multi-label learning for crop leaf diseases recognition and severity estimation based on convolutional neural networks

Cited by 39 publications

References 36 publications

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Semi-supervised multi-label classification using an extended graph-based manifold regularization

Groundnuts Leaf Disease Recognition using Neural Network with Progressive Resizing

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