Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

Qian, Xiufeng; Zhang, Chengqi; Chen, Li; Li, Ké

doi:10.3389/fpls.2022.864486

Cited by 40 publications

(9 citation statements)

References 41 publications

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“…The successful performance of transformer in NLP tasks has resulted in its integration and use in the field of computer vision . For instance, the work conducted by Qian et al (2022) introduced a novel strategy for classifying maize leaf diseases using a vision transformer-based method. The authors of the study also gathered RGB images from publicly available databases and experimental fields, classifying them into four distinct categories: southern corn leaf blight, gray leaf spot, southern corn rust, and healthy specimens.…”

Section: Abstract Precision Agriculture Transformer Neural Network Ma...mentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Abstract Precision Agriculture Transformer Neural Network Ma...mentioning

confidence: 99%

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

2024

Frontiers Research Topics

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“…In (Qian et al, 2022) author proposed a self-attention method to find out various diseases of maize. The author used a self-attention mechanism to suppress background noise and focus on the lesion spots on the leaf.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Leaf Diseases of Different Crops using modified ResNet50

Memon,

Qabulio,

Kumar

et al. 2024

ABBDM

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Every country’s income depends heavily on the agriculture sector. Agriculture output is influenced by a wide range of factors, including crop health, weather, water availability, and type of land. Low output is primarily caused by crop disease. The Identification and treatment of diseases early on are very crucial. Traditional approaches don't offer a reliable evaluation. The spread of the disease must be determined using automated techniques. One of the cutting-edge technologies that aids in automating disease detection is deep learning. Deep learning has been used in numerous studies to detect crop diseases. The goal of this research is to effectively diagnose leaf diseases in all major crops using a novel deep learning model using a modified ResNet50. The research employed different deep learning models like VGG16, Inception V3 and modified ResNet50 on publicly available dataset of plant village. A novel approach has been proposed by extracting features from different models. The ResNet50 uses the skip connection which is considered to training and test error. The modified ResNet50 model helps add diversity in different crops for providing accuracy across all crops on public dataset of plant village. The dataset was obtained from a publicly available database on Kaggle. The dataset contains 13 different crops and a total of 38 diseases classes. On modified plant villages the proposed model generated a very good accuracy of 99.49% with precision of 0.966 and f score of 0.995. The proposed model is reliable for the classification of various leaf diseases in all the major crops.

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“…The other image enhancement method using image resizing, ltering, color space conversion, and histogram equalization was discussed by Ngugi et al [31]. Moreover, resizing [32] and data augmentation [33] approaches were also opted for image enhancement for maize leaves as well as for multiple crops. The other leaf disease detection system that detects diseases in sorghum leaf utilized basic image processing methods including, Edge detection, thresholding, and noise reduction [34].…”

Section: Related Workmentioning

confidence: 99%

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Handa

Krishan

2023

Preprint

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In this world of digitization, most of the data is in the form of images acquired using camera. Image enhancement plays a vital role in the quality improvement of digital images. In this work, a combined approach based on the contrast limited adaptive histogram equalization (CLAHE) and Retinex algorithm is proposed. It is a wavelet based Retinex algorithm with adaptive histogram equalization and gaussian filter. First, image is enhanced using CLAHE, image is decomposed using Daubechies wavelet and then followed by the Retinex algorithm, which used low frequency components to enhance the image. Lastly, a gaussian filter is used to smoothen the image. The dataset of maize leaf disease is used for the analysis of quality enhancement and denoising. It is clear from the results that the proposed method improves the quality by reducing the noise of the maize leaf images. Theses refined images can be used for maize leaves disease detection and classification system to achieve high accuracy.

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Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

Cited by 40 publications

References 41 publications

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

IoT, UAV, BCI Empowered Deep Learning models in Precision Agriculture

Identification of Leaf Diseases of Different Crops using modified ResNet50

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

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