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RISS 인기검색어
- 검색키워드
- 저자 : ( Arumugam Kalaikannan ) (검색결과 1 건)
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Automated Strawberry Flower Detection for Yield Estimation using Machine Vision
( Arumugam Kalaikannan ),( Won Suk Lee ),( Natalia Peres ),( Clyde Fraisse ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Strawberry is an important horticultural crop in the U.S. Strawberry, which ranks 8th in produce and 4th in fruit with a total value of $2.4 billion annually. Since strawberries are manually harvested and labor shortage is a major concern, yield predictions become extremely important for scheduling labors in harvesting and other field operations as well as marketing. For efficient use of labor resources in harvesting and other operations, strawberry growers in the U.S. will need reliable yield prediction models by utilizing a more feasible and efficient method to count the number of flowers in their fields. Images were acquired from a strawberry field using a consumer grade digital color camera at various working distances, angles and lighting conditions. Various image processing techniques were used to develop automated flower counting algorithms from the images. An accuracy of 88% was achieved by the computer vision algorithm on validation dataset. The number of strawberry flowers obtained from this algorithm will be used to develop a strawberry yield prediction model, which eventually will be used for efficient labor management for harvesting and marketing to increase yield and profit of the strawberry growers.
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