The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
John H Lienhard - One of the best experts on this subject based on the ideXlab platform.
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brackish water desalination for Greenhouse agriculture comparing the costs of ro ccro edr and monovalent selective edr
Desalination, 2020Co-Authors: Kishor G Nayar, John H LienhardAbstract:Abstract Greenhouses are a rapidly growing agricultural sector that uses desalination systems. However, the desalination requirements of the Greenhouse Industry and an economic evaluation of desalination technologies for Greenhouses have not been reported previously. Several Greenhouse operators in North America using desalination systems were interviewed to identify key design specifications. A detailed cost comparison was conducted for key technologies: reverse osmosis (RO), closed circuit RO (CCRO), electrodialysis reversal (EDR) and monovalent selective EDR (MS-EDR). Capital, energy and membrane replacement costs, savings in feedwater costs from operating at higher recovery, and potential savings in fertilizer from using MS-EDR, were calculated. For 10-hectare Greenhouses, alternatives can be considered. MS-EDR is economically competitive if it can retain at least 20% of the calcium and magnesium needed for growing and if membranes last 7 years. CCRO is competitive if the sum of feedwater and brine disposal costs are >$0.24/m3. If this cost was $0.32/m3, additional investment over RO for CCRO, EDR and MS-EDR, could pay itself back in 2.4, 3.4 and 2.1 years. In Ventura county where municipal water costs $1.05/m3, RO, CCRO, EDR and MS-EDR had payback periods of 7.1, 8.4, 7.8 and 8.2 months.
Kishor G Nayar - One of the best experts on this subject based on the ideXlab platform.
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brackish water desalination for Greenhouse agriculture comparing the costs of ro ccro edr and monovalent selective edr
Desalination, 2020Co-Authors: Kishor G Nayar, John H LienhardAbstract:Abstract Greenhouses are a rapidly growing agricultural sector that uses desalination systems. However, the desalination requirements of the Greenhouse Industry and an economic evaluation of desalination technologies for Greenhouses have not been reported previously. Several Greenhouse operators in North America using desalination systems were interviewed to identify key design specifications. A detailed cost comparison was conducted for key technologies: reverse osmosis (RO), closed circuit RO (CCRO), electrodialysis reversal (EDR) and monovalent selective EDR (MS-EDR). Capital, energy and membrane replacement costs, savings in feedwater costs from operating at higher recovery, and potential savings in fertilizer from using MS-EDR, were calculated. For 10-hectare Greenhouses, alternatives can be considered. MS-EDR is economically competitive if it can retain at least 20% of the calcium and magnesium needed for growing and if membranes last 7 years. CCRO is competitive if the sum of feedwater and brine disposal costs are >$0.24/m3. If this cost was $0.32/m3, additional investment over RO for CCRO, EDR and MS-EDR, could pay itself back in 2.4, 3.4 and 2.1 years. In Ventura county where municipal water costs $1.05/m3, RO, CCRO, EDR and MS-EDR had payback periods of 7.1, 8.4, 7.8 and 8.2 months.
Anna Petropoulou - One of the best experts on this subject based on the ideXlab platform.
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Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production
Sensors, 2019Co-Authors: Silke Hemming, Feije De Zwart, Isabella Righini, Anne Elings, Anna PetropoulouAbstract:The global population is increasing rapidly, together with the demand for healthy fresh food. The Greenhouse Industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous Greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a Greenhouse growing experiment. Each team had a 96 m2 modern Greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a Greenhouse. One team outperformed the manually-grown reference.
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Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production
Sensors, 2019Co-Authors: Silke Hemming, Feije De Zwart, Isabella Righini, Anne Elings, Anna PetropoulouAbstract:The global population is increasing rapidly, together with the demand for healthy fresh food. The Greenhouse Industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous Greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a Greenhouse growing experiment. Each team had a 96 m2 modern Greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a Greenhouse. One team outperformed the manually-grown reference.
Victoria T. Barden - One of the best experts on this subject based on the ideXlab platform.
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The Virginia Commercial Greenhouse Industry- Current Practices and Future Needs Assessment
HortTechnology, 2004Co-Authors: Holly L. Scoggins, Joyce G. Latimer, Victoria T. BardenAbstract:This report summarizes responses to a survey of Virginia's commercial Greenhouse Industry, conducted in 2000-01. The survey included questions about interests and needs of growers to assist Virginia Tech Horticulture faculty and staff in planning educational and research programming. Respondents were asked about current cultural practices, future plans for automation and technology, and impact of issues facing the Greenhouse Industry such as regulations and labor. The 273 responses were categorized based on the amount of heated Greenhouse space: small, medium, large, or other (including part-time). Following analysis of the responses, focus groups were conducted across Virginia to further discuss issues raised in the survey.
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Profile of the Virginia Commercial Greenhouse Industry
HortTechnology, 2003Co-Authors: Holly L. Scoggins, Joyce G. Latimer, Victoria T. BardenAbstract:●‘Radiant’ (11.7%), ‘Royalty’ (8.5%), ‘Hopa’ (7.0%), ‘Indian Magic’ (6.6%), and ‘Snowdrift’ (5.7%) were the most commonly mentioned discontinued selections over all regions (Table 4). ‘Radiant’ and ‘Royalty’ ranked fi rst or second in the central, east-central and east regions, whereas ‘Hopa’ was the most frequently discontinued cultivar in the west-central region. Interestingly, japanese fl owering crab (M. fl ribunda) was the cultivar most often discontinued in the western region. Apple scab was identifi ed as the most prevalent crabapple disease across all regions (67.8%) except in the westcentral region, where fi re blight was considered most problematic (80.9%). The higher frequency of fi re blight is due to later blooming periods at higher elevations (Smith, 1998). Disease resistance has been stressed more than any other topic with regard to crabapples at the university level (Iles and Stookey, 1997). However, respondents in our study believe their retail and commercial clients are more concerned with fl owers and growth habit. A promotional campaign should be modeled after the Perennial Plant Association’s (PPA, Hilliard, Ohio) perennial of the year to highlight the best crabapple selections. Suggested selections may change as climactic conditions change across the United States, thus encouraging the use of the best cultivars and species available.
Silke Hemming - One of the best experts on this subject based on the ideXlab platform.
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Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production
Sensors, 2019Co-Authors: Silke Hemming, Feije De Zwart, Isabella Righini, Anne Elings, Anna PetropoulouAbstract:The global population is increasing rapidly, together with the demand for healthy fresh food. The Greenhouse Industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous Greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a Greenhouse growing experiment. Each team had a 96 m2 modern Greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a Greenhouse. One team outperformed the manually-grown reference.
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Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production
Sensors, 2019Co-Authors: Silke Hemming, Feije De Zwart, Isabella Righini, Anne Elings, Anna PetropoulouAbstract:The global population is increasing rapidly, together with the demand for healthy fresh food. The Greenhouse Industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous Greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a Greenhouse growing experiment. Each team had a 96 m2 modern Greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a Greenhouse. One team outperformed the manually-grown reference.
