Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

Memon, Muhammad Suleman; Kumar, Pardeep; Iqbal, Rizwan

doi:10.3390/computers11070102

Cited by 25 publications

(3 citation statements)

References 37 publications

(46 reference statements)

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“…However, it is difficult to obtain images of rotary kiln flames: the lack of sample data makes it difficult to meet the demand for image feature extraction in deep learning networks, and the training is prone to overfitting. To reduce the dependence of deep learning models on the number of training samples, transfer learning can be applied to classification recognition tasks for speeding up training efficiency [ 8 ]. Therefore, a combination of deep learning and transfer learning is applied to the recognition of rotary kiln working conditions.…”

Section: Introductionmentioning

confidence: 99%

Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln

Zheng

Wang

Qin

2022

Entropy

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It is difficult to identify the working conditions of the rotary kilns due to the harsh environment in the kilns. The flame images of the firing zone in the kilns contain a lot of working condition information, but the flame image data sample size is too small to be used to fully extract the key features. In order to solve this problem, a method combining transfer learning and attention mechanism is proposed to extract key features of flame images, in which the deep residual network is used as the backbone network, the coordinate attention module is introduced to capture the position information and channel information on the branch of feature graphs, and the features of flame images obtained are further screened to improve the extraction ability. At the same time, migration learning is performed by the pre-trained ImageNet data set, and feature migration and parameter sharing are realized to cope with the training difficulty of a small sample data size. Moreover, an activation function Mish is introduced to reduce the loss of effective information. The experimental results show that, compared with traditional methods, the working condition recognition accuracy of rotary kilns is improved by about 5% with the proposed method.

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Section: Introductionmentioning

confidence: 99%

Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln

Zheng

Wang

Qin

2022

Entropy

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“…Compared with the high accuracy of the existing literature (Li et al, 2020;Liang, 2021;Santos-Rufo and Rodriguez-Jurado, 2021;Sivakumar et al, 2021;Elaraby et al, 2022;Memon et al, 2022), the accuracy of the identification of roots suffering from cotton VW disease was about 4% lower in this paper. Studies in the literature have targeted leaves and stem with obvious disease symptoms, such as leaf yellowing and wilting.…”

Section: Figurementioning

confidence: 59%

Early detection of cotton verticillium wilt based on root magnetic resonance images

Tang

Xiao

et al. 2023

Front. Plant Sci.

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Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton.

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“…The efficacy of coffee leaf disease management depends on the disease's early discovery and accurate identification [7]. Disease identification and providing the required nutrients have become an important part of farming [8]. There is a substantial risk of making a false diagnosis when making a diagnosis with an unaided eye.…”

Section: Introductionmentioning

confidence: 99%

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Aufar¹,

Abdillah²,

Romadoni³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.

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Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

Cited by 25 publications

References 37 publications

Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln

Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln

Early detection of cotton verticillium wilt based on root magnetic resonance images

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

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