Agricultural Vegetation Monitoring Based on Aerial Data Using Convolutional Neural Networks

Ganchenko, Valentin; Doudkin, A. A.

doi:10.3103/s1060992x1902005x

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…However, compared to other available technologies, visible cameras provide lower performance to detect diseases at their early stages due to many factors. They are extremely susceptible to environmental conditions such as sunlight angle and shadows that could affect the crop disease identification system causing false disease detection Ganchenko and Doudkin [ 29 ]. The authors in Li et al [ 49 ] used another type of visible camera called RGB-D that is less sensitive to light.…”

Section: Uav-based Visual Remote Sensing Systems Used To Identify Cro...mentioning

confidence: 99%

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

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Section: Uav-based Visual Remote Sensing Systems Used To Identify Cro...mentioning

confidence: 99%

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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“…[20] Regressão Logística (RL) [21] entrada [5], [6], [7], [8]. As CNNs apresentam bom desempenho com acurácia acima de 99%, resultado obtido em outro conjunto de dados, o qual consiste em um estudo de detecção automática em um sistema de visão para avaliação da qualidade da limpeza robótica de plantas de processamento de pescado [9].…”

Section: Algoritmounclassified

Classificação de Folhas Nativas do Cerrado Usando Aprendizado de Máquina

Faria¹,

Bambil²,

Café³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Este trabalho compara 8 algoritmos de aprendizado de máquina em função da sua acurácia média de classificação de 30 espécies de plantas. O sistema de classificação utiliza fotos de folhas como entrada para o sistema de classificação. Os resultados evidenciam que o algoritmo Análise de Discriminante Linear se mostrou o mais apropriado para este problema específico, chegando a 98,46% de acurácia média.

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“…Convolutional neural networks are one of the most widely used deep supervised learning models in a wide spectrum of remote sensing applications and have achieved extraordinary improvement in recent years in the classification of remotely sensed data [52,53]. The use of diverse CNNs in crops and plant phenology recognition [54][55][56][57][58][59], weed detection [60][61][62], agriculture [51,63], vegetation mapping [64][65][66][67][68], tree crown detection and mapping [69][70][71][72], and disease detection [73][74][75][76][77] has elicited considerable interest.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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Agricultural Vegetation Monitoring Based on Aerial Data Using Convolutional Neural Networks

Cited by 6 publications

References 11 publications

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

Classificação de Folhas Nativas do Cerrado Usando Aprendizado de Máquina

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

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