Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

Rehman, Tanzeel U.; Mahmud, Sultan; Chang, Young K.; Jin, Jian Xun; Shin, Jaemyung

doi:10.1016/j.compag.2018.12.006

Cited by 317 publications

(137 citation statements)

References 144 publications

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“…For successful robotic harvesting, the robot must detect the fruit, reach the fruit, determine if the fruit is mature, detach the mature fruit from the plant, and transfer it to a container [ 2 ]. Most agricultural robotics research and development projects [ 3 , 4 , 5 ] focused on detecting [ 6 , 7 , 8 ], reaching [ 4 , 9 , 10 ], and detaching the fruit [ 4 , 9 ], with only a few studies focusing on maturity level determination [ 11 , 12 , 13 ]. Since different fruits can be in different maturity stages within the field and even on the same plant/branch, maturity classification is essential to enable selective harvesting [ 3 ] and an important element of an intelligent fruit-picking robot.…”

Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.

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Section: Introductionmentioning

confidence: 99%

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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“…It conducts classification by first calculating the distance between the test sample and all the training samples to obtain its nearest neighbors and then conducting the classification. The predefined "k" closest points in the training data are used for calculating the class probability and assigning the test sample to the class with the largest probability [34]. This method is very popular for its simple implementation and good classification performance [35].…”

Section: The K-nearest Neighbor Methodsmentioning

confidence: 99%

Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System

García

Candelo-Becerra²,

Hoyos

2019

Applied Sciences

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There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

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“…The combined capabilities of mobile devices along with appropriate algorithm can lead to automated disease diagnoses. Moreover, these approaches are based on machine learning and computer vision to classify diseases using only images of plants [5].…”

Section: Satwinder Kaur Garima Joshi Renu Vig Panjab University Chmentioning

confidence: 99%

Plant Disease Classification using Deep Learning Google Net Model

2019

IJITEE

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Plant diseases have been a major crisis that is disturbing the food production. So there is a need to provide proper procedures for plant disease detection at its growing age and also during harvesting stage. Timely disease detection can help the user to respond instantly and sketch for some defensive actions. This detection can be carried out without human intervention by using plant leaf images. Deep learning is progressively best for image detection and classification. In this effort, a deep learning based GoogleNet architecture is used for plant diseases detection. The model is trained using public database of 54,306 images of 14 crop varieties and their respective diseases. It achieves 97.82% accuracy for 14 crop types making it capable of further deployment in a crop detection and protection application.

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Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

Cited by 317 publications

References 144 publications

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System

Plant Disease Classification using Deep Learning Google Net Model

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