Individual leaf extractions from young canopy images using Gustafson–Kessel clustering and a genetic algorithm

Neto, J. Camargo; Meyer, George E.; Jones, David D.

doi:10.1016/j.compag.2005.11.002

Cited by 74 publications

(37 citation statements)

References 24 publications

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“…Fuzzy logic, cluster algorithms, and cluster reassembly routines mimic human perception and decision-making and tend to work well for extracting convex leaf shapes from plant canopy images (Neto et al 2006). However, for more botanically diverse leaf shapes, such as species with complex leaves, lobed margins (indented), and trifoliolates, new fitness criteria must be developed to accommodate various leaf shapes.…”

Section: Plant Recognitionmentioning

confidence: 99%

Future Directions for Automated Weed Management in Precision Agriculture

Young

Meyer

Woldt

2013

Automation: The Future of Weed Control in Cropping Systems

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In cropping systems, integrated weed management is based on diversification. Rather than relying solely on one or two herbicides, a multiplicity of weed control strategies is employed. Yet, integrated weed management as currently practiced is far from integrated; every weed is still managed the same regardless of location or season. The recent development of precision application technology is now allowing for smaller treatment units by making applications according to site-specific demands. The automated systems of the future will have sensor and computer technologies that first categorize each and every plant in the field as either weed or crop and then identify the species of weed. Following identification, multiple weed control tools located on a single platform are applied at micro-rates to individual plants based on their biology. For example, if the system identified a weed resistant to Roundup, it could be spritzed with a different herbicide or nipped with an onboard cutter or singed with a burst of flame. This system and others like it will be capable of targeting different weed-killing tools to specific weeds. This chapter will discuss the challenges and tools of the future. ( 2 0 1 4 ) Y o u n g , M e Y e r , & W o l d t i n A u t o m A t i o n : t h e F u t u r e o F W e e d C o n t r o l

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Section: Plant Recognitionmentioning

confidence: 99%

Future Directions for Automated Weed Management in Precision Agriculture

Young

Meyer

Woldt

2013

Automation: The Future of Weed Control in Cropping Systems

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“…The first step is creating a binary image which accurately separates plant regions from background. The second step is to use the binary template to isolate individual leaves as sub images from the original set of plant pixels (Camargo Neto, et al, 2006a). A third step was to apply a shape feature analysis to each extracted leaf (Camargo Neto, et al, 2006b).…”

Section: Machine Visionmentioning

confidence: 99%

“…Zadeh intensification of the membership functions resulted in definitive green canopy areas. A machine vision system with unsupervised image analysis and mapping of features was presented by Camargo Neto (2006a) and Camargo Neto, et al (2006b). A classification system was trained using statistical discriminant analysis which was tested using individual test leaves and clusters from several plants.…”

Section: Plant Species Classificationmentioning

confidence: 99%

Machine Vision Identification of Plants

Meyer¹

2011

Recent Trends for Enhancing the Diversity and Quality of Soybean Products

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“…using the 'watershed algorithm', Lee & Slaughter, 2004) and colour (e.g. using genetic algorithms -Neto, Meyer & Jones, 2006). Shearer & Holmes (1990) used texture analysis of plant top views to achieve canopy characterisations without extracting individual leaves.…”

Section: Segmentation In Natural Outdoor Scenesmentioning

confidence: 99%

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

McCarthy

Hancock

Raine

2010

Intel Serv Robotics

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Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight.From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.

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Individual leaf extractions from young canopy images using Gustafson–Kessel clustering and a genetic algorithm

Cited by 74 publications

References 24 publications

Future Directions for Automated Weed Management in Precision Agriculture

Future Directions for Automated Weed Management in Precision Agriculture

Machine Vision Identification of Plants

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

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