Identification and Location of Picking Tomatoes Based on Machine Vision

Xiao-lian, LV; Liu, Xiaorong; Bing-fu, LU

doi:10.1109/icicta.2011.316

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Apart from bruise detection, yield estimation, and disease identification, algorithms are also shown in maturity evaluation and acquisition of crop segmentation. In maturity evaluation, both a Fuzzy model [62] and medium filter algorithm [85] are employed for 3108 images on banana samples and 100 images on tomato samples with an average identification rate of 93.11%, and within 89% to 98%, respectively. The Fuzzy model is useful in handling ambiguous information for the banana fruit maturity detection using red-green-blue (RGB) components.…”

Section: Optics and Photonics Applications In Agriculturementioning

confidence: 99%

Applications of Photonics in Agriculture Sector: A Review

et al. 2019

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The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

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Section: Optics and Photonics Applications In Agriculturementioning

confidence: 99%

Applications of Photonics in Agriculture Sector: A Review

et al. 2019

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“…1 (2017) pp. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] cases (Figure8(a)). PLS Regression gave the minimum RMSE of 0.218 and maximum correlation coefficient (R p ) of 0.855 for FOS features set (Figure 8(a)) followed by 0.869 for GLCM feature set (Figure 8(b)).…”

Section: Firmness Predictionmentioning

confidence: 99%

“…Until date, MV has been widely applied to solve various agricultural problems, ranging from simple quality evaluation [17,18] to complex robot-steered applications [19][20][21]. Consumers on the other hand, grade quality of food products in a fuzzy way according to their senses such as sight, touch, smell etc.…”

Section: Introductionmentioning

confidence: 99%