Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks

Tetila, Everton Castelão; Machado, Bruno Brandoli; Menezes, Gabriel Kirsten; Oliveira, Adair da Silva; Álvarez, Marco; Amorim, Willian Paraguassu; Belete, Nícolas Alessandro de Souza; Silva, Gercina Gonçalves da; Pistori, Hemerson

doi:10.1109/lgrs.2019.2932385

Cited by 118 publications

(76 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The studied crops included maize [21][22][23][24][25][26], rice [27][28][29][30], wheat [31][32][33][34], barley [33,35], oat [36], soybeans [37,38], beans [39], spinach [39], vine [40][41][42][43], sugar beet [26], oilseed rape [44], sunflowers [45][46][47][48][49][50], cotton [51], grass [52], and meadows [53][54][55][56]. All fields except meadows were cultivated in a monocultural way.…”

Section: Study Topicsmentioning

confidence: 99%

“…Figure 1 (a) presents the main flying altitudes in relation to article publication years, with one main altitude per publication. The highest altitude (excluded from Figure 1) was 400 m [44], and the lowest altitudes were 1 m [34] and 2 m [37]. Three different altitude categories may be identified with equal distribution: (1) close range imaging at 1-25 m, optimised for spotting detailed information, often from individual images; (2) low-altitude imaging, 25-50 m being currently optimal for optics, especially with multispectral cameras; and (3) highaltitude drone imaging at 70-120 m, optimised for the mapping of large areas.…”

Section: Uav Imaging Campaignsmentioning

confidence: 99%

“…The average ground sample distance (GSD) in the reported studies was 4 cm, and 30% of the studies had a GSD smaller than 1 cm. The lowest GSD in the data capture phase was 25 cm with grapevine pests [42], and the highest reported resolution was 30,000 pixels per soybean leaf [37] collected with a Sony Exmor RGB camera (Sony, Tokyo, Japan). The imaging resolutions are examined in more detail in the next chapter.…”

Section: Uav Imaging Campaignsmentioning

confidence: 99%

See 2 more Smart Citations

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

View full text Add to dashboard Cite

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

show abstract

Section: Study Topicsmentioning

confidence: 99%

Section: Uav Imaging Campaignsmentioning

confidence: 99%

Section: Uav Imaging Campaignsmentioning

confidence: 99%

See 1 more Smart Citation

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A fine-tuned GoogLeNet model was proposed by Li et al [3] to recognize their collected pest dataset and obtained an improvement of 6.22% compared to the state-of-the-art method. Tetila et al [4] presented an analysis of the network weights for the automatic recognition of soybean leaf diseases applied to images taken straight from a small and cheap unmanned aerial vehicle (UAV). They evaluated four deep neural network models trained with different parameters for finetuning (FT) and transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network

et al. 2020

View full text Add to dashboard Cite

The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented superresolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications.

show abstract

“…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). Semantic segmentation of unmanned aerial vehicle (UAV) images, the task we undertake here, has been implemented in soybean (Tetila et al, 2019), tea plants (Gensheng et al, 2019), and maize .…”

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.

View full text Add to dashboard Cite

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.

show abstract

Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks

Cited by 118 publications

References 18 publications

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network

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

Contact Info

Product

Resources

About