Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

Ha, Jin Gwan; Moon, Hyeonjoon; Kwak, Jin Tae; Hassan, Syed Ibrahim; Dang, Minh; Lee, O New; Park, Han Yong

doi:10.1117/1.jrs.11.042621

Cited by 85 publications

(47 citation statements)

References 26 publications

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“…Recall = TP TP + FN (8) where TP, FP, and FN are the numbers of true positives, false positives, and false negatives detected for each image. High accuracy means fewer false positives.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Therefore, improving the throughput of phenotyping measurements is a significant challenge in this kind of research. Recent developments in the application of the unmanned aerial vehicle (UAV) mounted with high definition cameras have increased the sample size tremendously [8][9][10]. Researchers have implemented many applications in plant height estimation [11][12][13], seedling counting [14][15][16], and crop growth estimation [17,18] using UAV images.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

et al. 2020

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Maize tassels play a critical role in plant growth and yield. Extensive RGB images obtained using unmanned aerial vehicle (UAV) and the prevalence of deep learning provide a chance to improve the accuracy of detecting maize tassels. We used images from UAV, a mobile phone, and the Maize Tassel Counting dataset (MTC) to test the performance of faster region-based convolutional neural network (Faster R-CNN) with residual neural network (ResNet) and a visual geometry group neural network (VGGNet). The results showed that the ResNet, as the feature extraction network, was better than the VGGNet for detecting maize tassels from UAV images with 600 × 600 resolution. The prediction accuracy ranged from 87.94% to 94.99%. However, the prediction accuracy was less than 87.27% from the UAV images with 5280 × 2970 resolution. We modified the anchor size to [852, 1282, 2562] in the region proposal network according to the width and height of pixel distribution to improve detection accuracy up to 89.96%. The accuracy reached up to 95.95% for mobile phone images. Then, we compared our trained model with TasselNet without training their datasets. The average difference of tassel number was 1.4 between the calculations with 40 images for the two methods. In the future, we could further improve the performance of the models by enlarging datasets and calculating other tassel traits such as the length, width, diameter, perimeter, and the branch number of the maize tassels.

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“…Recall = TP TP + FN (8) where TP, FP, and FN are the numbers of true positives, false positives, and false negatives detected for each image. High accuracy means fewer false positives.…”

Section: Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

et al. 2020

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“…For the first step, the combination of DL with other advanced technologies, such as unmanned aerial vehicles (UAVs), radar, and Internet of Things, can provide high quality datasets of images and other forms. These data greatly enhance applications of DL in agriculture and improve the accuracy of resulting tools [84] . For the second step, new training algorithms and methods can enhance the accuracy, especially for those applications that require high precision.…”

Section: Discussionmentioning

confidence: 98%

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Zhu¹,

Liu²,

Liu³

et al. 2018

International Journal of Agricultural and Biological Engineering

141

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In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

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“…Compared to the level of interest, relatively few examples have been published. Machine learning classification has been used to classify entire plants as virus-infected or not (Ha et al, 2017;Sugiura et al, 2018). Object detection methods have been used to identify diseased regions of grape plants (Kerkech et al, 2018) and diseased leaves of soybean (Tetila et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data

Wiesner-Hanks

Stewart

et al. 2019

Front. Plant Sci.

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Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.

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Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

Cited by 85 publications

References 26 publications

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data

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