Plant leaf disease classification using EfficientNet deep learning model

Atila, Ümit; Uçar, Murat; Akyol, Kemal; Uçar, Emine

doi:10.1016/j.ecoinf.2020.101182

Cited by 507 publications

(217 citation statements)

References 27 publications

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“…Existing plant diseases and pests classification network mostly use the muture network structures in computer vision, including AlexNet [ 26 ], GoogleLeNet [ 27 ], VGGNet [ 28 ], ResNet [ 29 ], Inception V4 [ 30 ], DenseNets [ 31 ], MobileNet [ 32 ] and SqueezeNet [ 33 ]. There are also some studies which have designed network structures based on practical problems [ 34 – 37 ]. By inputting a test image into the classification network, the network analyses the input image and returns a label that classifies the image.…”

Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

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Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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“…The data sets are also divided into sections that are easy to understand. For example, the work of [32] shows the divisions of the work in terms of the diseases like Rice Blast (RB), Bacterial leaf Blight (BLB), and Sheath Blight (SB). The use of a PlantVillage data set was also applied in the research by [32].…”

Section: Plant Disease Image Data Setsmentioning

confidence: 99%

“…For example, the work of [32] shows the divisions of the work in terms of the diseases like Rice Blast (RB), Bacterial leaf Blight (BLB), and Sheath Blight (SB). The use of a PlantVillage data set was also applied in the research by [32]. The data set consists of 54,306 images of 14 different crops representing 26 plant diseases.…”

Section: Plant Disease Image Data Setsmentioning

confidence: 99%

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Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Khan

Albattah

et al. 2021

Wireless Communications and Mobile Computing

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Plant disease automation in agriculture science is the primary concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Moreover, the increased use of technology today has increased the efficacy and accuracy of detecting diseases in plants and animals. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. Some diseases are also transmitted between animals and human beings, making it hard to fight them. For many years, scientists have researched how to deal with the common diseases that affect humans and plants. However, there are still many parts of the detection and discovery process that have not been completed. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. Our focus is to clarify the details about the diseases and how to detect them promptly with artificial intelligence. We discuss the use of machine learning and deep learning to detect diseases in plants automatically. Our study also focuses on how machine learning methods have been moved from conventional machine learning to deep learning in the last five years. Furthermore, different data sets related to plant diseases are discussed in detail. The challenges and problems associated with the existing systems are also presented.

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“…In the deep learning of images, width, depth, and resolution are the main factors affecting the prediction accuracy of neural networks. EfficientNet considers not only the adjustment of a single dimension [23][24][25] but also the comprehensive expansion of width, depth, and resolution. It also balances the forecast accuracy and resource footprint of the network.…”

Section: Pattern Recognition Of Porcelain Insulator Surface State Based On Efficientnetmentioning

confidence: 99%

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

Zhao¹,

Jing²,

Wang³

et al. 2021

Entropy

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A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.

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Plant leaf disease classification using EfficientNet deep learning model

Cited by 507 publications

References 27 publications

Plant diseases and pests detection based on deep learning: a review

Plant diseases and pests detection based on deep learning: a review

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

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