Review of Recent Machine-Vision Technologies in Agriculture

Ji, Bin; Zhu, Weixing; Liu, Bo; Ma, Chenguang; Li, Xianfeng

doi:10.1109/kam.2009.231

Cited by 11 publications

(10 citation statements)

References 36 publications

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“…Automated and robotic systems could have faster acceptance by farmers if their safety aspects are well fulfilled [ 34 ]. Several reviews [ 35 , 36 , 37 ] on autonomous navigation of agricultural vehicles have been written, however, there was little focus on the 3-D vision approach.…”

Section: Applications In Agriculturementioning

confidence: 99%

3-D Imaging Systems for Agricultural Applications—A Review

Vázquez-Arellano

Griepentrog

Reiser

et al. 2016

Sensors

217

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Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, natural or artificial markers, and obstacles. Currently, three dimensional (3-D) sensors are economically affordable and technologically advanced to a great extent, so a breakthrough is already possible if enough research projects are commercialized. The aim of this review paper is to investigate the state-of-the-art of 3-D vision systems in agriculture, and the role and value that only 3-D data can have to provide information about environmental structures based on the recent progress in optical 3-D sensors. The structure of this research consists of an overview of the different optical 3-D vision techniques, based on the basic principles. Afterwards, their application in agriculture are reviewed. The main focus lays on vehicle navigation, and crop and animal husbandry. The depth dimension brought by 3-D sensors provides key information that greatly facilitates the implementation of automation and robotics in agriculture.

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Section: Applications In Agriculturementioning

confidence: 99%

3-D Imaging Systems for Agricultural Applications—A Review

Vázquez-Arellano

Griepentrog

Reiser

et al. 2016

Sensors

217

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“…The imaging technique is analogous to the function of the human eye. It captures the image of the subject for necessary calculations and measurements before performing the final evaluations [7]. The imaging technique is essential for collecting spatial, color [6] and even thermal [8] information of the subject of interest.…”

Section: Classification Of Photonics Systems In Agriculturementioning

confidence: 99%

Applications of Photonics in Agriculture Sector: A Review

et al. 2019

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The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

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“…Modern agriculture is applying new scientific and technological processes to automate and provide more accurate and appropriate solutions to real problems in agricultural systems [1]. Machine vision represents one of these technological achievements, which is being integrated into the agricultural field because it is considered as accurate and nondestructive and yields consistent results [2].…”

Section: Introductionmentioning

confidence: 99%

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

Hernández-Rabadán

Ramos-Quintana

Juk

2014

The Scientific World Journal

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This work presents a methodology that integrates a nonsupervised learning approach (self-organizing map (SOM)) and a supervised one (a Bayesian classifier) for segmenting diseased plants that grow in uncontrolled environments such as greenhouses, wherein the lack of control of illumination and presence of background bring about serious drawbacks. During the training phase two SOMs are used: one that creates color groups of images, which are classified into two groups using K-means and labeled as vegetation and nonvegetation by using rules, and a second SOM that corrects classification errors made by the first SOM. Two color histograms are generated from the two color classes and used to estimate the conditional probabilities of the Bayesian classifier. During the testing phase an input image is segmented by the Bayesian classifier and then it is converted into a binary image, wherein contours are extracted and analyzed to recover diseased areas that were incorrectly classified as nonvegetation. The experimental results using the proposed methodology showed better performance than two of the most used color index methods.

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Review of Recent Machine-Vision Technologies in Agriculture

Cited by 11 publications

References 36 publications

3-D Imaging Systems for Agricultural Applications—A Review

3-D Imaging Systems for Agricultural Applications—A Review

Applications of Photonics in Agriculture Sector: A Review

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

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