FWDGAN-based data augmentation for tomato leaf disease identification

Li, Mingxuan; Zhou, Guofu; Chen, Aibin; Yi, Jizheng; Lu, Chao; He, Mingyi; Hu, Yahui

doi:10.1016/j.compag.2022.106779

Cited by 25 publications

(9 citation statements)

References 11 publications

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“…A summary of the related work on plant disease identification based on leaf images and the result comparison of some of them is shown in Table 11 . As can be seen from both Table 11 and Figure 13 , our model outperformed the previous models, surpassing that of Li et al [ 58 ], which was the closest in performance, with a +0.75% performance gain. Whereby some authors used the accuracy metric to measure their performance, we recorded a much higher accuracy but chose to benchmark against our F1 score value, which is regarded as a much better form of performance measure for classification problems, since it combines both the precision and recall of the model in question.…”

Section: Evaluation Metrics Results and Discussionmentioning

confidence: 51%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

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Section: Evaluation Metrics Results and Discussionmentioning

confidence: 51%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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“…In formula (5), P represents the target detection accuracy of the network (in this paper, the recognition accuracy of citrus, apple, grape, and other fruit targets) and R represents the recall rate of the network. e precision in the evaluation index refers to the average value of each correct detection rate in the fruit target detection samples.…”

Section: Self-comparison Testmentioning

confidence: 99%

“…e precision in the evaluation index refers to the average value of each correct detection rate in the fruit target detection samples. TP in equation (5) indicates that the predicted value is positive and the actual value is positive. From the perspective of the calculation formula, F1 value is the weighted average of the accuracy of the model and the recall rate of the model.…”

Section: Self-comparison Testmentioning

confidence: 99%

“…With the popularization of intelligent agriculture, agricultural intellectualization is developing rapidly. Among them, the robot for citrus, apple, grape, and other fruit picking technology and fruit disease detection has become a research hotspot [4,5]. Rapid and accurate identi cation and detection of fruit targets are of great signi cance to achieving automatic fruit picking, fruit yield prediction, and intelligent management of the orchard industry.…”

Section: Introductionmentioning

confidence: 99%

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Fruit Target Detection Based on BCo-YOLOv5 Model

Yang

Yao

et al. 2022

Mobile Information Systems

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After the birth of deep learning, artificial intelligence has entered a vigorous period of rapid development. In this process of rising and growing, we have made one achievement after another. When deep learning is applied to fruit target detection, due to the complex recognition background, large similarity between models, serious texture interference, and partial occlusion of fruits, the fruit target detection rate based on traditional methods is low. In order to solve these problems, a BCo-YOLOv5 network model is proposed to recognize and detect fruit targets in orchards. We use YOLOv5s as the basic model for feature image extraction and target detection. This paper introduces BCAM (bidirectional cross attention mechanism) into the network and adds BCAM between the backbone network and the neck network of the YOLOv5s basic model. BCAM uses weight multiplication strategy and maximum weight strategy to build a deeper position feature relationship, which can better assist the network in detecting fruit targets in fruit images. After training and testing the network, the map BCo-YOLOv5 network model reaches 97.70%. In order to verify the detection ability of the BCo-YOLOv5 network to citrus, apple, grape, and other fruit targets, we conducted a large number of experiments BCo-YOLOv5 network. The experimental results of the BCo-YOLOv5 network show that this method can effectively detect citrus, apple, and grape targets in fruit images, and the fruit target detection method based on BCo-YOLOv5 network is better than most orchard fruit detection methods.

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“…The adverse effects caused by abnormal samples are mitigated through the normalization ability of SELU. Furthermore, the negative semi-axis of SELU is no longer set to 0, which solves the problem of nerve death in RELU [47]. It is worth noting that when SCAE adopts SELU as the activation function, the As can be seen from Figures 12 and 13, for different variants of AE, the optimal activation function is different.…”

Section: The Selection Of Activation Function For Aementioning

confidence: 99%

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

Cheng

Jiao

Yan

et al. 2022

Sensors

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For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model.

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FWDGAN-based data augmentation for tomato leaf disease identification

Cited by 25 publications

References 11 publications

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Fruit Target Detection Based on BCo-YOLOv5 Model

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

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