A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network

Li, Dengshan; Wang, Rujing; Xie, Chengjun; Liu, Liu; Zhang, Jie; Li, Rui; Wang, Fangyuan; Zhou, Man; Liu, Wancai

doi:10.3390/s20030578

Cited by 180 publications

(97 citation statements)

References 43 publications

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“…To validate the accurate performance of a classifier, researchers should test its ability to differentiate similar symptoms related to different diseases (e.g., Northern leaf blight and anthracnose leaf blight in maize leaves). To achieve high accuracy, models should be trained with the target disease dataset [ 122 ] and images of similar symptoms on different organs (e.g., withered stems and leaves in rice) [ 121 ], as well as the typical viral symptoms in leaves of (melon, zucchini, cucurbit, cucumber, papaya watermelon, cucumber) [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Section: Discussionmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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“…Dengshan Li et al [20] proposed a mechanism that detects rice leaf disease from real-time video using deep learning techniques. They used faster-RCNN for image detection from video and also used various deep CNN models like VGG16, ResNet-50, ResNet-101, and YOLOv3.…”

Section: Related Workmentioning

confidence: 99%

“…Dengshan Li et al [20] They declared their proposed system could be applied to other rice disease and pests S. Ramesh et al [18] In the future, to enhance the detection and classification of paddy diseases, any improvement method can be used to get the best performance by decreasing the false prediction.…”

Section: Author Information Limitationsmentioning

confidence: 99%

An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Islam¹,

Nymur²,

Shamsojjaman³

et al. 2021

IJACSA

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Bangladesh and India are significant paddycultivation countries in the globe. Paddy is the key producing crop in Bangladesh. In the last 11 years, the part of agriculture in Bangladesh's Gross Domestic Product (GDP) was contributing about 15.08 percent. But unfortunately, the farmers who are working so hard to grow this crop, have to face huge losses because of crop damages caused by various diseases of paddy. There are approximately more than 30 diseases of paddy leaf and among them, about 7-8 diseases are quite common in Bangladesh. Paddy leaf diseases like Brown Spot Disease, Blast Disease, Bacterial Leaf Blight, etc. are very well known and most affecting one among different paddy leaf diseases. These diseases are hampering the growth and productivity of paddy plants which can lead to great ecological and economical losses. If these diseases can be detected at an early stage with great accuracy and in a short time, then the damages to the crops can be greatly reduced and the losses of the farmers can be prevented. This paper has worked on 4 types of diseases and one healthy leaf class of the paddy. The main goal of this paper is to provide the best results for paddy leaf disease detection through an automated detection approach with the deep learning CNN models that can achieve the highest accuracy instead of the traditional lengthy manual disease detection process where the accuracy is also greatly questionable. It has analyzed four models such as VGG-19, Inception-Resnet-V2, ResNet-101, Xception, and achieved better accuracy from Inception-ResNet-V2 is 92.68%.

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“…Since the start of 2020, articles have been published using convolutional neural networks (CNNs) to detect patterns in LiDAR (Light Detection and Ranging) data, images in the Google Street View database, video data, UAV data, and NASA's Earth Observation (EO) data for a variety of purposes from detecting pedestrians at night to mapping landslides [19][20][21][22][23]. There have been successful efforts using CNN's to detect buried landmines in ground-penetrating radar data, yet there is a lack of research on using CNN to identify surface mines such as the PFM-1 [24,25].…”

Section: Convolutional Neural Network (Cnn) Overviewmentioning

confidence: 99%

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

et al. 2020

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Recent advances in unmanned-aerial-vehicle-(UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection and identification of scatterable antipersonnel landmines in wide-area surveys. Our methodology is calibrated for the detection of scatterable plastic landmines which utilize a liquid explosive encapsulated in a polyethylene or plastic body in their design. We base our findings on analysis of multispectral and thermal datasets collected by an automated UAV-survey system featuring scattered PFM-1-type landmines as test objects and present results of an effort to automate landmine detection, relying on supervised learning algorithms using a Faster Regional-Convolutional Neural Network (Faster R-CNN). The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and 71.5% testing accuracy for a completely withheld testing set. Across multiple test environments, using centimeter scale accurate georeferenced datasets paired with Faster R-CNN, allowed for accurate automated detection of test PFM-1 landmines. This method can be calibrated to other types of scatterable antipersonnel mines in future trials to aid humanitarian demining initiatives. With millions of remnant PFM-1 and similar scatterable plastic mines across post-conflict regions and considerable stockpiles of these landmines posing long-term humanitarian and economic threats to impacted communities, our methodology could considerably aid in efforts to demine impacted regions.

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A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network

Cited by 180 publications

References 43 publications

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

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