Analysis of yield components and dry matter production in a simplified soilless tomato culture system by using controlled-release fertilizers during summer–winter greenhouse production

Kinoshita, Takafumi; Yamazaki, H.; Inamoto, Katsuhiko; Yamazaki, Hiroko

doi:10.1016/j.scienta.2016.02.019

Cited by 19 publications

(18 citation statements)

References 30 publications

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“…Therefore, predicting whether the EC will increase or decrease in the future can help with sophisticated nutrient control. Because nutrient solution control depends on a contemporary EC in current soilless cultures (Neto et al, 2014; Kinoshita et al, 2016), predicted 3-h EC might improve the accuracy of nutrient control. In addition, since the RMSE did not change much near the test accuracy of 0.08 after the 9 h, it can be said that stable forecasts during a day are possible.…”

Section: Discussionmentioning

confidence: 99%

“…Although various prediction methods have been developed, nutrient control systems are usually based on contemporary EC monitoring and are vulnerable to ion balance in root-zone nutrient solutions (Neto et al, 2014; Kinoshita et al, 2016). These limits result from crops influencing changes in EC and from growth environments (Dewir et al, 2005; Stutte, 2006; Shin and Son, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information

2018

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In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information

2018

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“…The two experiments were conducted in a greenhouse (120 m 2 ) covered with polyolefin film at the NARO Tohoku Agricultural Research Center, Morioka, Iwate, Japan (39°45′N, 141°8′E) during 2014-2015 (Expt.1) and -2016. Prior to flowering, tomato seedlings (Solanum lycopersicum) were transplanted to a growing bed (80 cm length, 31.2 cm width, and 7.5 cm height) filled with a mixed substrate consisting of cedar bark (approximately 50% in volume), akadama (red clay granular), pumice, and zeolite, as described by Kinoshita et al (2016), to form a single line of plants. The distance between the plants was 20 cm.…”

Section: General Cultural Practicesmentioning

confidence: 99%

“…The individual leaf area (LA, cm 2 ) of each cultivar was obtained using the following regression equation: LA = a LW, where "a" represents a proportionality factor for each cultivar (0.317, "Super Yubi"; 0.346, "Momotaro York"; 0.355, "Rinka 409"). The regression equations (R 2 = 0.903 to 0.945, P < 0.01 for all regressions) were obtained by destructive sampling conducted several times during the experiments as described by Higashide and Heuvelink (2009) and Kinoshita et al (2016).…”

Section: Measurementsmentioning

confidence: 99%

Effects of Interplanting on Fruit Yield and Dry Matter Production in Greenhouse-grown Tomato by IntegratingTwo Different Crop Periods

Kinoshita

Yamazaki

Inamoto

2019

JARQ

Self Cite

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We investigated the effects of interplanting on fruit yield in greenhouse-grown tomato by integrating the first (short) and second (long) crop periods in the cooler northern area of Japan. The marketable fruit yield was similar regardless of interplanting in the first crop period, whereas it increased with earlier planting dates in the second and total crop periods. Therefore, planting immediately after the harvest of the first fruit trusses of the first crop period was effective in achieving a continuous harvest and substantial yield improvement upon conclusion of the harvest of six trusses; moreover, there were no incidences of stem lowering in the first crop period. The plant dry weight was also similar regardless of interplanting in the first crop period. However, the total dry weight and fruit dry weight increased with early interplanting in the second and total crop periods. The marketable fruit yield and total dry matter production of the whole crop period increased when cultivation started in March compared with that in April. The dry matter allocation to the fruit was similar among the treatments in all crop periods. The total fresh and dry fruit yields were significantly correlated with the cumulative light interception over the total crop period. Therefore, fruit yield was the highest when cultivation started in March with interplanting in the early-summer resulting in the highest light interception and dry matter production.

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“…Greenhouse agriculture achieves great success in many countries like Netherlands (Korthals Altes and van Rij, 2013), Israel (Teitel and Zhao, 1992; Elad et al, 2014), Japan (Kinoshita et al, 2016), and the United States (Burnett et al, 2016). China has the world's largest area of greenhouse agriculture, however, more than 90% of the greenhouses use primitive facilities, and soil culture is still the main method for crop production (Du, 2007).…”

Section: Introductionmentioning

confidence: 99%

Growth, Water Use, and Nitrate-15N Uptake of Greenhouse Tomato as Influenced by Different Irrigation Patterns, 15N Labeled Depths, and Transplant Times

Ming

Jin

et al. 2017

Front. Plant Sci.

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Increasing water use efficiency and reducing nitrogen pollutant discharge are important tasks for modern agriculture. To evaluate the effect of alternate partial root-zone irrigation (APRI) on tomato plant growth, water use efficiency and nitrate-15N uptake, an experiment was conducted from June to December in 2014 under greenhouse condition in northern China. The experiment contained two irrigation patterns [APRI and conventional irrigation (CI)], two 15N labeled depths in soil (10 and 50 cm) and two transplant time (early and late summer). Results showed that, compared to CI, APRI did not significantly (p > 0.05) impact the growth and biomass accumulation in aboveground part of tomato, while it enhanced the root, reflecting by greater length density, and more dry mass. APRI produced marginally lower yields, but saved 34.9% of irrigation water, and gave a 37.6–49.9% higher water use efficiency relative to CI. In addition, APRI improved fruit quality, mainly through increasing the contents of soluble solid (by 12.8–21.6%), and vitamin C (2.8–12.7%), and the sugar/acid ratio (3.5–8.5%). The 15N utilization efficiency (15NUE) in APRI was higher than that in CI, which was more evident when 15N was labeled at 50 cm depth. Significant (p < 0.05) 15N recovery increase of 10.2–13.2% and 15N loss decrease of 35.4–54.6% were found for APRI compared to CI. The overall results suggest that APRI under greenhouse could benefit the nitrate-N recovery and increase the water use efficiency in tomato.

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Analysis of yield components and dry matter production in a simplified soilless tomato culture system by using controlled-release fertilizers during summer–winter greenhouse production

Cited by 19 publications

References 30 publications

Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information

Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information

Effects of Interplanting on Fruit Yield and Dry Matter Production in Greenhouse-grown Tomato by IntegratingTwo Different Crop Periods

Growth, Water Use, and Nitrate-15N Uptake of Greenhouse Tomato as Influenced by Different Irrigation Patterns, 15N Labeled Depths, and Transplant Times

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