Robotics and intelligent machines in agriculture

Sistler, Frederick E.

doi:10.1109/jra.1987.1087074

Cited by 72 publications

(44 citation statements)

References 1 publication

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“…Schertz and Brown (1968) first proposed the concept of automated harvesting as an alternate to mechanical harvesting. Sistler (1987) argued that research and development on machine vision, robotics, and intelligent machine is key to improve the sustainability of specialty crop production in USA. Pejsa and Orrock (1984) and Sistler (1987) indicated that citrus fruit and apple has greatest potential for fruit detection and robotic harvesting.…”

Section: Introductionmentioning

confidence: 99%

Sensors and systems for fruit detection and localization: A review

Gongal

Amatya

Karkee

et al. 2015

Computers and Electronics in Agriculture

447

244

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Section: Introductionmentioning

confidence: 99%

Sensors and systems for fruit detection and localization: A review

Gongal

Amatya

Karkee

et al. 2015

Computers and Electronics in Agriculture

447

244

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“…Agriculture sector being the most important sector to mankind has not adopted automation completely. The reason being insufficient knowledge of the farmers on electronic and automation equipment and use of machineries throughout the year [4]. Traditional agricultural methods practiced by the local farmers are highly sustainable, although the all-inclusive cost is not cheap [5].…”

Section: Literature Survey:-mentioning

confidence: 99%

Resource and Area Management With Automated Olericulture Nursery Monitoring System.

Manoj¹,

Raj²,

Pavithra³

et al. 2017

IJAR

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“…However, even the most sophisticated machines, which perform multiple automation tasks in the manufacturing, and mining industries cannot perform the same tasks in the agriculture industry because of the diversity in the agriculture industry (Anonymous 2011b. There is diversity both in the product, like non-uniform shape, size and colour and in the environment like varied terrain, ambient conditions, temperature, humidity and dust (Sistler 1987).…”

Section: Automation In the Agricultural Industrymentioning

confidence: 99%

Automation of Unloading Grain Cars Using the “Grain-o-Bot”

2013

Trans. ASABE

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Large quantities of bulk grain are moved using graincars in Canada and other parts of the world. Automation has not progressed significantly in the grain industry probably because the market is limited for automated systems. A prototype of a robot ("Grain-o-bot") using machine vision to automatically open and close graincar hopper gates and detect the contents of the graincar was built and studied. The "Grain-o-bot" was a Cartesian robot equipped with two cameras and an opening tool as the end-effector.One camera acted as the eye to determine the sprocket location, and guided the endeffector to the sprocket opening.For most applications, machine vision solutions based on pattern recognition were developed using images acquired in a laboratory setting. Major constraints with these solutions occurred when implementing them in real world applications. So the first step for this automation was to correctly identify the hopper gate sprocket on the grain car.Algorithms were developed to detect and identify the sprocket under proper lighting conditions with 100% accuracy. The performance of the algorithms was also evaluated for the identification of the sprocket on a grain car exposed to different lighting conditions, which are expected to occur in typical grain unloading facilities.Monochrome images of the sprocket from a model system were acquired using different light. Correlation and pattern recognition techniques using a template image combined with shape detection were used for sprocket identification. The images were preprocessed using image processing techniques, prior to template matching. The template image developed from the light source that was similar to the light source used to acquire ii images was more successful in identifying the sprocket than the template image developed using different light sources.A sample of the graincar content was taken by slightly opening and immediately closing the hopper gates. The sample was identified by taking an image using the second camera and performing feature matching. An accuracy of 99% was achieved in identifying Canada Western Red Spring (CWRS) wheat and 100% for identifying barley and canola.iii ACKNOWLEDGEMENTSWith profound indebtedness, I wish to acknowledge my deep sense of gratitude to Dr. Digvir Singh Jayas for giving me an opportunity to pursue my doctoral research under his supervision, his excellent guidance, and support.

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Robotics and intelligent machines in agriculture

Cited by 72 publications

References 1 publication

Sensors and systems for fruit detection and localization: A review

Sensors and systems for fruit detection and localization: A review

Resource and Area Management With Automated Olericulture Nursery Monitoring System.

Automation of Unloading Grain Cars Using the “Grain-o-Bot”

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