Comparison of Growth Characteristics and Yield by Sweet Pepper Varieties at Glass Greenhouse in Reclaimed land and Farms

Um, Yeong Cheol; Choi, Chang Kyoung; Seo, Tae Cheol; Lee, Jun Gu; Jang, Yoon Ah; Kim, Tae Hyun; Oh, Sang Seok; Lee, Hye Jin

doi:10.14397/jals.2013.47.6.33

Cited by 5 publications

(5 citation statements)

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“…The average length of sweet pepper leaves was 223 mm. The average leaf width was 121 mm [32] and the average diameter of sweet pepper fruit was 80.92 mm, suitable for obtaining overlapping images of each sweet pepper part at the corresponding distance. [30].…”

Section: Hardware Compositionmentioning

confidence: 99%

“…According to 2, the area of the imaging system in this study overlapped from a maximum of 53 minimum of 230 mm. The average length of sweet pepper leaves was 223 mm average leaf width was 121 mm [32] and the average diameter of sweet pepper fru 80.92 mm, suitable for obtaining overlapping images of each sweet pepper part corresponding distance. The device for obtaining images of the sweet peppers was designed as sho Figure 3a.…”

Section: Hardware Compositionmentioning

confidence: 99%

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Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

et al. 2021

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For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers.

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Section: Hardware Compositionmentioning

confidence: 99%

Section: Hardware Compositionmentioning

confidence: 99%

Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

et al. 2021

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“…The cutting part of the end-effector is designed to be carried out vertically, thus preventing damage to the stem of the sweet pepper. The end-effector was designed according to the characteristics of sweet peppers cultivated in South Korea with reference to the studies of Kim et al [23], Ministry of Agriculture, Food and Rural Affairs (MAFRA) [24] and Um et al [25]. The horizontal length of the end-effector is set up at 120 mm, and 150% of the sweet pepper's average diameter 80.92 mm.…”

Section: Hardware Compositionmentioning

confidence: 99%

A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment

et al. 2019

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Recently, farmers of sweet pepper suffer from the increase of its unit production costs due to the rise of labor costs. The rise of unit production costs of sweet pepper, on the other hand, decreases its productivity and causes the lack of its farming expertise, thus resulting in the quality degradation of products. In this regard, it is necessary to introduce an automated robot harvest system into the farming of sweet pepper. In this study, the authors developed an image-based closed-loop control system (a vision servo system) and an automated sweet pepper harvesting robot system and then carried out experiments to verify its efficiency. The working area of the manipulator that detects products through an imaging sensor in the farming environment of sweet pepper, decides whether to harvest it or not, and then informs the location of the product to the control center, which is set up at the distance scope of 350~600 mm from the center of the system and 1000 mm vertically. In order to confirm the performance of the sweet pepper recognition in this study, 269 sweet pepper images were used to extract fruits. Of 269 sweet pepper images, 82.16% were recognized successfully. The harvesting experiment of the system developed in this study was carried out with 100 sweet peppers. The result of experiment with 100 sweet peppers presents the fact that its approach rate to peduncle is about 86.7%, and via four sessions of repetitive harvest experiment it achieves a maximal 70% harvest rate, and its average time of harvest is 51.1 s.

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“…Um et al(2013)의 보고에서 여름 작형 파프리카 생산량이 8월 460kg•ha -1 •day -1 로 최저였다는 결과와 유사하다. 이는 상•하반기의 광과 온도 환경이 달랐기 때문으로 상반기의 광량을 100%로 했을 때 하반기는 60-70% 수준으로 상대적으로 약광인데 반해 7월 한 달간 매일 고온으로 유지된 온실 내부로 인하여 낙화와 낙과를 유발하였을 것으로 생각된다.…”

unclassified

“…즉, 일평균 일사량이 1MJ•m -2 증가하면 생산량은 약 0.7g 많아지고, 주간 온도가 1°C 상승하면 생산량은 0.4g 많아지는 반면, 야간 온도가 1°C 상승하면 생산량은 0.8g 감소하고, 일평균 온도가 1°C 상승하면 생산량은 0.5g 감소함 을 의미한다. 이러한 결과는 다른 연구 결과에서도 보고되었다 Um et al(2013)…”

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Effects of Greenhouse Environmental Factors on the Productivity of Summer-cultivated Sweet Pepper and Influential Factors

Kim,

et al. 2023

HST

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This study was conducted to analyze the effects of summer greenhouse environmental factors on the productivity of sweet pepper (Capsicum annuum L.) and to determine the influential factors. Two sweet pepper export farms in Gangwon-do (F In in Inje and F Ch in Cheorwon) were surveyed during 2021. The flowering of sweet pepper started in March. F In undertook two-stemmed cultivation for 35 weeks and F Ch utilized three-stemmed cultivation for 31 weeks. The amount of accumulated solar radiation gradually increased from the first flowering period, peaking in July, after which it decreased to about 60% due to frequent rainfall in August. The temperature was high in July, with the day temperature exceeding 30°C and the night temperature being over 21°C. The night temperature increased to as high as 26°C from July 4 to August 13. An analysis of the growth characteristics showed that F In has a shorter flower length, a thinner stem diameter, and a shorter internode compared to F Ch . The relative internode ratio of F In was 138% for group 1, which is significantly higher than that of F Ch . However, the ratio became lower than that of F Ch from group 2 and significantly shorter for group 4, reaching 80%. The second half productions of F In and F Ch were 36.1 and 39.9% of the total production rates, respectively. Both farms showed sharply low fruit-set rates in group 5 along with very low harvest rates in groups 4 and 5. It was noted that the low fruit-set rate and harvest rates for groups 4 and 5 led to low yields in the second half of the year. Multiple regression analysis results showed that daytime temperatures and solar radiation levels were positively linked to production levels, while night-time temperatures and growing degree days (GDD) (24h-mean temperature minus 10) were negatively linked. Greater influences were

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Comparison of Growth Characteristics and Yield by Sweet Pepper Varieties at Glass Greenhouse in Reclaimed land and Farms

Cited by 5 publications

References 0 publications

Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment

Effects of Greenhouse Environmental Factors on the Productivity of Summer-cultivated Sweet Pepper and Influential Factors

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