Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

Zhang, Dapeng; Zhang, Tieyan; Ji, Jianwei; Sun, Zenghui; Wang, Yonggang; Sun, Yuqing; Li, Qingji

doi:10.3390/s20010155

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…These results coincided with previous results showing that the proper droplet size provides more carbohydrate and photochemical energy to speed up nitrate accumulation [64] . The results also revealed that the soluble sugar and protein was low at 15 min of spray interval, it was maximum at 30 min of spray interval and then decreased with increasing the spray intervals under all atomizers [65,66] . The finding of this study concluded that a combination of air-assisted atomizers and 30 min of spray interval could be beneficial to improve the nutritional quality of lettuce.…”

Section: Nutritional Quality Of Lettucementioning

confidence: 86%

Effects of droplet size and spray interval on root-to-shoot ratio, photosynthesis efficiency, and nutritional quality of aeroponically grown butterhead lettuce

Tunio¹,

Gao²,

Qureshi³

et al. 2022

International Journal of Agricultural and Biological Engineering

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The proper selection of the atomizer (droplet size) and nutrient solution spray interval is one of the most important factors to be investigated in aeroponics systems for sustainable agriculture. The aim of this study was to research the effects of four aeroponics atomizing nozzles (one air-assisted; A 1 , two air-less; A 2 and A 3 , and one ultrasonic fogger; A 4 ) with droplet sizes of 11.24 µm, 26.35 µm, 17.38 µm, and 4.89 µm, respectively, four spray intervals (15 min (I 1 ), 30 min (I 2 ), 45 min (I 3 ) and 60 min (I 4 )) at a 5 min of constant spray time by atomizing the Hoagland's nutrient solution on growth, root-to-shoot ratio, photosynthesis characteristics, pigments, and nutritional quality of the aeroponically grown lettuce. The experimental results demonstrated that in A 1 atomizer and I 2 interval, the growth, photosynthesis efficiency, chlorophyll, carotenoids, and nutritional values of the lettuce were significantly higher compared to that grown in A 2 and A 3 atomizers at all spray intervals. The shoot developments were more constrained than root, prominent to the alteration of root-to-shoot ratio (fresh and dry) in the influence of different droplet sizes and spray intervals. Moreover, the plants did not grow well in A 4 atomizer associated with proposed spray intervals. The results disclosed that there was an obvious interaction between droplet sizes (atomizers) and spray intervals for growth, the ratio of root to shoot, photosynthesis efficiency, pigments, and nutritional quality of the aeroponically grown lettuce. This research study increases the awareness of the proper droplet size (atomizer) and the regulation of nutrient solution spray interval for leafy vegetables grown in an aeroponics system.

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Section: Nutritional Quality Of Lettucementioning

confidence: 86%

Effects of droplet size and spray interval on root-to-shoot ratio, photosynthesis efficiency, and nutritional quality of aeroponically grown butterhead lettuce

Tunio¹,

Gao²,

Qureshi³

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…The experiment was carried out in a Liaoshen Type III solar greenhouse built in an east-west orientation. The greenhouse was 60 m long and 12 m wide [19]. The heights of the northern wall and northern roof were 3 and 5.5 m, respectively.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

et al. 2021

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Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy of tomato yield in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters and the tomato yield is the prediction output. The GA is adopted to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation (BP) neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability compared with the BP and the WNN prediction models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.

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“…Environmental control is a key factor to increase plant productivity in controlled environment agriculture (CEA) [1]. Recently, increased interest has been directed towards plant production in closed facilities (e.g., plant factories, vertical farms, indoor-growing modules) [2][3][4]. However, with the introduction of advanced monitoring and control technologies, it becomes necessary to properly discern plant/microclimate interaction, modulate environmental parameters, and manage cultivation factors.…”

Section: Introductionmentioning

confidence: 99%

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models

Amitrano

Chirico

Pascale

et al. 2020

Sensors

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Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm−2), edible biomass (RMSE = 6.87 g m−2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76–68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.

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Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

Cited by 4 publications

References 48 publications

Effects of droplet size and spray interval on root-to-shoot ratio, photosynthesis efficiency, and nutritional quality of aeroponically grown butterhead lettuce

Effects of droplet size and spray interval on root-to-shoot ratio, photosynthesis efficiency, and nutritional quality of aeroponically grown butterhead lettuce

Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models

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