Design Farming Robot for Weed Detection and Herbicides Applications Using Image Processing

Bhong, Vijay S.; Waghmare, Dhanashri L.; Jadhav, Akshay; Bahadure, Nilesh Bhaskarrao; Bhaldar, Husain; Vibhute, Anup

doi:10.1007/978-3-030-16848-3_38

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…More promising is the development of ground based mobile pollinators (Amador & Hu, 2017;Ohi et al, 2018;Williams et al, 2019). In a similar vein a large amount of research has also been done for mobile based weed spraying robots (Berenstein et al, 2010;Bhong et al, 2020;Burgos-Artizzu et al, 2011;Fennimore et al, 2016;Lee et al, 1999;López-Granados, 2011;McAllister et al, 2019;Reiser et al, 2019;Slaughter et al, 2008;Urdal et al, 2014). In each application, the autonomous vehicle is attempting to detect, locate, and spray pollen or herbicide onto either a flower or weed within the orchard.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the quality of kiwifruit pollinated with an autonomous robot

Williams¹,

Bell²,

Nejati³

et al. 2021

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The growing popularity of kiwifruit orchards in New Zealand is increasing the already high demand for traditional pollinators (bees), with alternatives currently too costly for most growers due to high labour requirements or inefficient usage of expensive pollen. A novel pollinating robot has been previously described to provide a more efficient, reliable and cost-effective means of addressing this problem. However, the pollinator suffered from a low fruit-set rate of 40% overall, well below commercial requirements of 80% to 90%. This paper presents two new developments for that system: a new, off the shelf spray nozzle (SS1504) to increase the overall pollen delivery and an automated height controller to keep the spray manifolds at a consistent distance below the canopy while avoiding obstacles. Furthermore, we have designed and conducted a more controlled, real-world evaluation of the pollination system to compare both nozzle variants and measure the commercial viability of the pollination platform. Final results show that, operating from a mobile platform at 2.5 km h −1 , the new nozzle could consistently achieve a fruit-set rate only 16 ± 2% below the control samples in each test orchard (82 ± 2% and 72 ± 4% absolute). Pollen consumption remains high, however, with estimates of up to 4.6 kg ha−1 , thus preventing the system from being economically viable. While cost-effectiveness awaits substantial efficiency improvements, our work has demonstrated an automated pollinator that can produce commercial-grade kiwifruit.

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Section: Related Workmentioning

confidence: 99%

Evaluating the quality of kiwifruit pollinated with an autonomous robot

Williams¹,

Bell²,

Nejati³

et al. 2021

View full text Add to dashboard Cite

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Weed Detection on Carrots Using Convolutional Neural Network and Internet of Thing Based Smartphone

Patria,

Sambas,

Sulaiman

et al. 2024

IAPGOS

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This study proposes a method based on Convolutional Neural Network (CNN) for automated detection of weed in color image format. The image is captured and transmitted to the Internet of Thing (IoT) server following an HTTP request made through the internet which is made available using the GSM based modem connection. The IoT Server save the image inside server drive and the results are displayed on the smartphone (Vision app). The results show that carrot and weed detection can be monitored accurately. The results of the study are expected to provide assistance to farmers in supporting smart farming technology in Indonesia.

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Design Farming Robot for Weed Detection and Herbicides Applications Using Image Processing

Cited by 2 publications

References 3 publications

Evaluating the quality of kiwifruit pollinated with an autonomous robot

Evaluating the quality of kiwifruit pollinated with an autonomous robot

Weed Detection on Carrots Using Convolutional Neural Network and Internet of Thing Based Smartphone

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