Design of automatic cotton picking robot with Machine vision using Image Processing algorithms

Rao, U. S. N.

doi:10.1109/care.2013.6733700

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…This paper also proposes at the new algorithms in Image processing of the cotton to extract the feature, modeling and matching. These are artificial intelligence for robotic vision, image processing for segmentation, feature measurement such as invariants, size and shape, texture and scene analysis and controlling the robotic arms in desired angle (Rao, 2013). The identification of mature cotton is one of the critical tasks in this robotic application.…”

Section: Global Cotton Production Harvesting and Ginning Trendsmentioning

confidence: 99%

Multiple Attributed Parametric Review Study on Mechanical Cotton (Gossypium hirsutum L.) Harvesters

Chandel¹,

Sharma²

2022

JAS

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Crop characteristics of cotton are crucial to identify the important crop attributes like plant height, canopy width, sympods and monopods distribution, row spacing which affects the performance of mechanical harvesters. The activity and effectiveness of most harvest aids, including desiccants is reduced by low temperature conditions. Trash content was observed to be lesser in cotton harvested by cotton picker than cotton harvested by cotton stripper. It was found that a maximum cotton yield of 1000 kg acre-1 was obtained for a cotton plant population ranging between 45,000 and 90,000 plants acre-1. Likewise, a minimum of 700 to 740 kg acre-1 was observed for a cotton plant population of 33,000 plants acre-1. In higher yielding cotton, cotton pickers recorded higher picking rate than cotton strippers. Picking/harvesting efficiency of cotton stripper with both finger and brush type mechanism was higher than the spindle type cotton picker. Picking efficiency of pneumatic picker was higher than the other types of picking mechanisms, but with lesser rate of picking capacity. Gin turnout of cotton was higher with cotton picker when compared with cotton stripper due to lesser trash content in picker harvested cotton. The horsepower requirement of cotton stripper ranged from ½ to ¼ horsepower and cost is about two-thirds of the price as compared with cotton picker. The scheduling and monitoring of various activities involved in cotton picking by using a suitable software model can increase the benefits of both growers and harvesting companies. The reduction in uniformity with roller gin-type lint cleaners ranged between 0.2 to 0.8%, which was lesser as compared with saw-type lint cleaners. Introducing mechanical harvesting has always been a decades-long process. In Turkey, it took 20 years and in Greece, this process took place very gradually over a 15-year period. Top cotton producing countries like India, Pakistan, China, Uzbekistan and other developing countries like Iran Paraguay are still not using machine harvesting. The introduction of mechanical cotton picker or stripper can help improve quality and quantity of cotton picking thereby giving more benefit to growers in developing countries and improving their socio-economic status. The most controversial issue raised by the introduction of the mechanical cotton harvester is great migration as the machines eliminated jobs and forced poor families to leave their homes and farms in search for urban jobs. Therefore Government policies towards cotton harvesting mechanization must include the alternative jobs, packages for dependent manual cotton pickers and their families.

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Section: Global Cotton Production Harvesting and Ginning Trendsmentioning

confidence: 99%

Multiple Attributed Parametric Review Study on Mechanical Cotton (Gossypium hirsutum L.) Harvesters

Chandel¹,

Sharma²

2022

JAS

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“…An enabling technology for frequent harvest will be the ability to use machine vision tools to detect cotton boll under various lighting conditions. The use of machine vision to identify cotton bolls is underway in the U.S. [65], India [66], and China [67]. Cotton is easier than other crops to identify for harvest as, when ready for harvest, it is white in contrast to green and brown backgrounds.…”

Section: Frequent Harvest Systemmentioning

confidence: 99%

Opportunities for Robotic Systems and Automation in Cotton Production

et al. 2021

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Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting.

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“…Rao [4] proposed design of automatic cotton picking robot with machine vision using image processing algorithm. In this a new cotton recognition method using a single camera is proposed, which is based on color subtraction of different parts of cotton plant.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on in Field Cotton Recognition for Cotton Harvesting Robot based on Image Processing Technique

Patel¹,

Bhalani²

2018

IJCA

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India is second largest country in production of cotton but it face problem like harvesting due to lack of skilled human labour. Solution of this problem is to do harvesting using cotton harvesting robots based on machine vision. Image processing technique has become very important in a wide variety of application. In farming is can be used to recognize cotton in field. Various image segmentation techniques are used for this operation. In field cotton recognition is challenging task because cotton plant contain various components like stem, leaves etc. Also open environment gives challenge to recognition task. Camera used to capture image of cotton plant, different camera view, illumination level at time of image capture etc. plays important role in the recognition of cotton in filed. Primary focus is to recognition of cotton in every environmental condition and also every possible mature cotton need to be detected. This review paper will give information about the possible image processing technique for recognition of cotton in field and it give future work done in the same topic.

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Design of automatic cotton picking robot with Machine vision using Image Processing algorithms

Cited by 11 publications

References 2 publications

Multiple Attributed Parametric Review Study on Mechanical Cotton (Gossypium hirsutum L.) Harvesters

Multiple Attributed Parametric Review Study on Mechanical Cotton (Gossypium hirsutum L.) Harvesters

Opportunities for Robotic Systems and Automation in Cotton Production

A Review on in Field Cotton Recognition for Cotton Harvesting Robot based on Image Processing Technique

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