Deep learning based real-time detection of northern corn leaf blight crop disease using YoloV4

Richey, Blake; Shirvaikar, Mukul

doi:10.1117/12.2587892

Cited by 13 publications

(6 citation statements)

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“…Liu et al [7] improved the YOLOv3 algorithm to detect tomato pests and diseases with a detection accuracy of 92.39 percent. Richey et al [8] enhanced the YOLOv4 algorithm to detect northern leaf percent. Richey et al [8] enhanced the YOLOv4 algorithm to detect northern leaf blight i maize, achieving an average detection accuracy of 93.55% for this particular disease.…”

Section: Introductionmentioning

confidence: 99%

A Study on Maize Leaf Pest and Disease Detection Model Based on Attention and Multi-Scale Features

Kang,

Zhang,

Xia

et al. 2023

Applied Sciences

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The detection and accurate positioning of agricultural pests and diseases can significantly improve the effectiveness of disease and pest control and reduce the cost of prevention and control, which has become an urgent need for crop production. Aiming at the low precision of maize leaf pest and disease detection, a new model of maize leaf pest and disease detection using attention mechanism and multi-scale features was proposed. Our model combines a convolutional block attention module (CBAM) with the ResNet50 backbone network to suppress complex background interference and enhance feature expression in specific regions of the maize leaf images. We also design a multi-scale feature fusion module that aggregates local and global information at different scales, improving the detection performance for objects of varying sizes. This module reduces the number of parameters and enhances efficiency by using a lightweight module and replacing the deconvolutional layer. Experimental results on a natural environment dataset demonstrate that our proposed model achieves an average detection accuracy of 85.13%, which is 9.59% higher than the original CenterNet model. The model has 24.296 M parameters and a detection speed of 23.69 f/s. Compared with other popular models such as SSD-VGG, YOLOv5, Faster-RCNN, and Efficientdet-D0, our proposed model demonstrates superior performance in the fast and accurate detection of maize leaf pests and diseases. This model has practical applications in the identification and treatment of maize pests and diseases in the field, and it can provide technical support for precision pesticide application. The trained model can be deployed to a web client for user convenience.

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Section: Introductionmentioning

confidence: 99%

A Study on Maize Leaf Pest and Disease Detection Model Based on Attention and Multi-Scale Features

Kang,

Zhang,

Xia

et al. 2023

Applied Sciences

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“…Not only that, convolutional neural networks will be used for the classification of the seven diseases most commonly found on strawberry trees. In this way, farmers who own Android smartphones will be able to make immediate use of the application and thus take better care of their crops [10].…”

Section: Introductionmentioning

confidence: 99%

Smart Farming with Computer Vision: Detecting Diseases in Strawberry Crops via Mobile Applications

Cruz,

Pieadade,

Grael

et al. 2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Strawberries are red fruits and an excellent choice for a healthy and delicious meal. They are produced from a sweet flower and are considered an excellent source of vitamin C, vitamin A, and potassium. Strawberry production in Brazil is concentrated in the states of São Paulo, Minas Gerais, and Rio Grande do Sul. According to Embrapa, in 2016, these states produced about 2.5 million tons, which corresponds to almost 60% of national production. Similar to other fruits, strawberries can be affected by various problems, such as diseases, pests, and adverse weather conditions. The use of pesticides in strawberry production is quite common, as strawberries are very susceptible to pests and diseases. However, pesticides can be harmful to human health, so it is important that they are used safely. Currently, diseases are detected by growers by eye and identified through the experience of the grower. However, less experienced farmers may encounter problems in identifying and even detecting diseases present in the crop. Computer Vision is a field of computing that studies how to represent and manipulate information about objects and environments in a way that allows computers to "see" the world similar to humans. The application of vision techniques for disease detection allows any farmer to obtain knowledge about the disease present in the plantation. This study proposes the use of Convolutional Neural Networks in mobile applications for the detection of diseases present in strawberry crops.

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“…Indeed, diverse diseases can rapidly be recognized, and the farmer can take the correct procedures to mitigate or eliminate the problem early. Several researchers have already developed and proposed solutions that make use of neural networks to detect various diseases in different plants such as corn [ 10 ], tomato [ 11 ], and many others [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.

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Deep learning based real-time detection of northern corn leaf blight crop disease using YoloV4

Cited by 13 publications

References 0 publications

A Study on Maize Leaf Pest and Disease Detection Model Based on Attention and Multi-Scale Features

A Study on Maize Leaf Pest and Disease Detection Model Based on Attention and Multi-Scale Features

Smart Farming with Computer Vision: Detecting Diseases in Strawberry Crops via Mobile Applications

Smart Strawberry Farming Using Edge Computing and IoT

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