Development of an evapotranspiration estimation method for lettuce via mobile phones using machine vision: Proof of concept

Zhangzhong, Lili; Gao, Hairong; Zheng, Wengang; Wu, Jianwei; Li, Jingjing; Wang, De-Qun

doi:10.1016/j.agwat.2022.108003

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The machine learning algorithm is very suitable for constructing multi-input and multi-output nonlinear system models, with differences in the principles of different types of machine learning leading to differences in model performance. This paper selects four kinds of machine learning models with good performance, including BP, SVR, RF and XG Boost [32][33][34][35].…”

Section: Multiobjective Optimization Methods Based On Machine Learningmentioning

confidence: 99%

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

et al. 2023

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To solve the problem that the parameters of the multiple-input multiple-output (MIMO) irrigation system are difficult to control accurately, an on-demand irrigation control experimental device was developed. The main input parameters of the device are the opening degree of the main pipe valve, the opening frequency of and the pump station, the opening degree of the branch pipeline valve with the different combinations of different opening degrees. Based on these input parameters, four types of experimental methods were designed, and a total of 1695 groups of experiments were designed. The results show that the different opening degree combinations of the branch electric valve cannot significantly affect the flow of the branch pipeline but also significantly affect the pressure of the main pipeline. The prediction error of the operating frequency of the pump station and the opening degree of the branch valve were regarded as the objective function. Six intelligent prediction models were constructed, which are Back Propagation (BP), support vector regression (SVR) Linear, SVR-RBF, SVR-Poly, random forest (RF) and eXtreme Gradient Boosting (XGBoost), respectively. The results show that the XGBoost is the best model among the six models. For the opening degree of three branch valves, the mean absolute error (MAE) between the predicted value and actual value is less than 3.3%, the mean square deviation (RMSE) between the predicted values and actual values is less than 4.5%, and the R2 of between the predicted values and actual values is greater than 0.990. The control models and system can meet the needs of an on-demand irrigation system.

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Section: Multiobjective Optimization Methods Based On Machine Learningmentioning

confidence: 99%

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

et al. 2023

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“…Effects of evapotranspiration in the circulation of atmosphere had a great impact on plant growth and yield and was proven as an effective basis for efficient irrigation management (Zhangzhong et al, 2023). Using LEDs as source of artificial light was more energy efficient than other light sources and could be controlled easily for optimum light intensity to improve plant growth.…”

Section: Optimized Artificial Light Propertiesmentioning

confidence: 99%

Optimization of Aquaponic Lettuce Evapotranspiration Based on Artificial Photosynthetic Light Properties Using Hybrid Genetic Programming and Moth Flame Optimizer

et al. 2023

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Land and water resources, climate change, and disaster risks significantly affect the agricultural sector. An effective solution for growing crops to improve productivity and optimize the use of resources is through controlled-environment agriculture (CEA). Evapotranspiration (ET) is an important greenhouse crop attribute that can be optimized for optimum plant growth. Light intensity and radiation are significant for controlling ET. To address this challenge, this study successfully determined the properties of optimum artificial light for minimum evapotranspiration rate of head development-stage and harvest-stage lettuce under light-period and dark-period using genetic programming and bio-inspired algorithms namely, grey wolf optimization (GWO), whale optimization algorithm (WOA), dragonfly algorithm (DA), and moth flame optimization (MFO). MFO provided the optimized global solution for the configured models. Results showed that head development-stage lettuce requires higher light intensity with lower visible to infrared radiation ratio (Vis/IR) than harvest-stage lettuce when exposed to light. On the other hand, harvest-stage lettuce requires higher light intensity with lower Vis/IR than head development-stage under dark-period respiration reaction. Findings of this study can be utilized in growing and improving yield crops in controlled-environment agriculture.

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Variation in vegetation water use efficiency at the basin scale: separating the contributions of climate change and human activities

Xie

et al. 2023

Environ Earth Sci

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Development of an evapotranspiration estimation method for lettuce via mobile phones using machine vision: Proof of concept

Cited by 6 publications

References 4 publications

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

Optimization of Aquaponic Lettuce Evapotranspiration Based on Artificial Photosynthetic Light Properties Using Hybrid Genetic Programming and Moth Flame Optimizer

Variation in vegetation water use efficiency at the basin scale: separating the contributions of climate change and human activities

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