Identification of weeds in sugarcane fields through images taken by UAV and Random Forest classifier

Yano, I. H.; Alves, José Ricardo; Santiago, Wesley Esdras; Mederos, Bárbara Janet Teruel

doi:10.1016/j.ifacol.2016.10.076

Cited by 36 publications

(22 citation statements)

References 14 publications

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“…The researchers have not used other statistical values such as kurtosis, skewness, variance, etc. because it generated 82% [16] accuracy which is less than the proposed methods. The other methods were not compared since the detection is done during the tilling stage.…”

Section: Resultsmentioning

confidence: 76%

“…A UAV was not only used to explore and analyze plant diseases in the vineyard [12] and sugarcane plots [13], but also classified the vegetation variety [14]. Moreover, there were studies which were conducted during the tilling stage of cultivation images for sugarcane yields assessment [15,16]. However, this research applied a UAV to assess sugarcane yields in the mature stage.…”

Section: Introductionmentioning

confidence: 99%

“…In this research, the researchers have used fewer features to classify the sugarcane areas. The researchers in [16] have proposed eight statistical values (mean, average deviation, standard deviation, variance, kurtosis, skewness, maximum, and minimum) whereby the accuracy is low compared to the methods used in this proposed research which use mean and standard deviation with 13 filtering masks.…”

Section: Introductionmentioning

confidence: 99%

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The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Tanut

Riyamongkol

2020

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This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect detection algorithm uses high-resolution sugarcane plantations and image processing techniques. The algorithm for defect detection consists of four processes: (1) data collection, (2) image preprocessing, (3) defect detection model creation, and (4) application program creation. For feature extraction, the researchers used image segmentation and convolution filtering by 13 masks together with mean and standard deviation. The feature extraction methods generated 26 features. The K-nearest neighbors algorithm was selected to develop a model for the classification of the sugarcane areas. The color selection method was also chosen to detect defect areas. The results show that the model can recognize and classify the characteristics of the objects in sugarcane plantation images with an accuracy of 96.75%. After the comparison with the expert surveyor’s assessment, the accurate relevance obtained was 92.95%. Therefore, the proposed model can be used as a tool to calculate the percentage of defect areas and solve the problem of evaluating errors of yields in the future.

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Section: Resultsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Tanut

Riyamongkol

2020

Information

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“…UAV provide real time, more high quality imagery data as compared to satellite imagery data (Zinkie and Flener, 2013), (Antle et al, 2015). UAV fly in low altitude, without cloud interference and can take higher resolution pictures (Zinkie and Flener, 2013), (Yano et al, 2016). UAV uses the combination of simple digital photographic cameras with spectral filters designed to provide multi-spectral images in the visible and infra-red domains (Kahan, 2007).…”

Section: Importance Of Uav In Provision Of Spatial Data and Informationmentioning

confidence: 99%

Development of Methods Acquiring Real Time Very High Resolution Agricultural Spatial Information Using Unmanned Aerial Vehicle

Bolo

Mpoeleng²,

Zlotnikova³

2019

AOL

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There is a need for high resolution spatial information to provide quality agricultural spatial information for better monitoring and management of farm activities to increase production and sustainable agricultural economic development. The Unmanned Aerial Vehicles are able to capture very high resolution spatial data that can be transformed into useful geospatial information, databases and digital maps. However, Unmanned Aerial Vehicle methods of acquiring spatial data are yet to be developed. The objective of this study was to develop methods of acquiring real time high resolution agricultural spatial data using Unmanned Aerial Vehicle. A qualitative case study research approach, and data collection method were used to achieve the objective. A ground truth data was carried out to eliminate errors. Unmanned Aerial Vehicle data acquisition system and data processing methods were developed. These methods could be used for better farm management and reduce the cost of inputs like fertilizers.

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“…Além disso, o uso de veículos aéreos não tripulados (VANTs) permite obter, apesar da baixa cobertura territorial, maiores detalhes físicos da vegetação em relação à imagens adquiridas por satélites. Esses detalhes por sí só, dependendo da escala e qualidade da imagem, podem ser capazes de descrever uma determinada espécie bem como mostram os trabalhos de David e Ballado (2016), Pérez-Ortiz et al (2016) e Yano et al (2016.…”

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Reconhecimento de capim-annoni em meio à forrageiras nativas do bioma Pampa por análise de textura

J¹

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Identification of weeds in sugarcane fields through images taken by UAV and Random Forest classifier

Cited by 36 publications

References 14 publications

The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Development of Methods Acquiring Real Time Very High Resolution Agricultural Spatial Information Using Unmanned Aerial Vehicle

Reconhecimento de capim-annoni em meio à forrageiras nativas do bioma Pampa por análise de textura

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