Influência Do Volume Médio Por Árvore Na Produtividade E Nos Custos De Produção Do Feller Buncher

Freitas, Luis Carlos de; Ferreira, Francisco de Assis Costa; Leite, Elton da Silva; Barros, Ana Paula da Silva; Luz, Danusia Silva; Virgens, Aline Pereira das

doi:10.22533/at.ed.4451916016

Cited by 2 publications

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“…Some events can boost the evolution of greenhouses, as is the case of west Portugal, increasing innovation as the greenhouses have been substituted and the brands expand their activity [10]. In this case, the boost of technological advances arrived with a natural disaster in 2009 that shredded several structures and forced the producers to rebuild, with State support, in an opportunity to rethink, improve and adopt more sustainable structures to achieve the best yields with the lowest environmental impacts [11].…”

Section: Introductionmentioning

confidence: 99%

Greenhouse Tomato Technologies and Their Influence in Mediterranean Region

Saraiva¹,

Dias²,

Grego³

et al. 2024

Tomato Cultivation and Consumption - Innovation and Sustainability

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Tomato (Solanum lycopersicum L.) is the most consumed vegetable and one of the most studied crops in the world. Over the years, several technologies have been studied and applied to crop production towards higher productivity, quality, and production efficiency. This chapter reviews greenhouse tomato production, cropping systems, and environmental conditioning, focusing on technological developments and the latest reclaimed water trends that have started to take off in the context of increasing water scarcity due to climate change. Following worldwide research trends and policies, the influence of the different technologies in fresh tomato production and the use of reclaimed water or reuse of treated nutrient solution is explored as it is expected to be a great advance in the Mediterranean region in the next years, and it is of the utmost importance, as the region increasingly suffers from climate change effects.

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Section: Introductionmentioning

confidence: 99%

Greenhouse Tomato Technologies and Their Influence in Mediterranean Region

Saraiva¹,

Dias²,

Grego³

et al. 2024

Tomato Cultivation and Consumption - Innovation and Sustainability

View full text Add to dashboard Cite

show abstract

“…In the south-east of Spain, Almería, home to the world's largest concentration of greenhouses (over 30,000 hectares), tomatoes are the main crop, accounting for 37.7% of all production [9]. Manual tomato harvesting is associated with low labour productivity because it is sporadic, fatiguing, with high to moderate physical effort and high repeatability by the operator, requiring about 700-900 h/year/ha [10], generating a low labour force attractiveness. Along with the scarcity of labour, the precarious working conditions and increased labour costs constrain the greenhouse harvesting operation.…”

Section: Introductionmentioning

confidence: 99%

Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato

et al. 2022

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The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.

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Influência Do Volume Médio Por Árvore Na Produtividade E Nos Custos De Produção Do Feller Buncher

Cited by 2 publications

References 0 publications

Greenhouse Tomato Technologies and Their Influence in Mediterranean Region

Greenhouse Tomato Technologies and Their Influence in Mediterranean Region

Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato

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