Process-based greenhouse climate models: Genealogy, current status, and future directions

Katzin, David; Henten, E.J. van; Mourik, S. van

doi:10.1016/j.agsy.2022.103388

Cited by 37 publications

(27 citation statements)

References 120 publications

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“…van Mourik et al (2019) used this approach, but the filters did not lead to improvement in monitoring. Furthermore, recently published greenhouse climate models were reviewed by Katzin et al (2022), and the authors noted there is still a lot of progress to be made on them, including regarding the assessment of their performances. Nevertheless, this is a case in which, differently from the assimilation of environmental measurements, assimilation of fruit observations would directly impact uncertainties in estimates, since they include external information regarding the state of the desired variable.…”

Section: Assimilation Of Observationsmentioning

confidence: 99%

Can Accuracy Issues of Low-Cost Sensor Measurements Be Overcome With Data Assimilation?

et al. 2023

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The use of mechanistic plant growth models relies on the availability of high-quality inputs to reduce uncertainty in estimates. Measurements of photosynthetically active radiation inside a protected environment are either more expensive to obtain or dependent on assumptions regarding external measurements. This study aimed to reduce the influence of uncertainty in the measurements of low-cost lux meters by using a data assimilation strategy. We first determined, by simulation, the impact of different sensors on the estimates. We then used the Ensemble Kalman Filter to assimilate artificial observations of tomato growth in the Reduced-State Tomgro model, in simulations for which the solar radiation inputs were obtained from a low-cost lux meter. We compared the assimilated estimates to the simulations that used solar radiation obtained with a scientific-grade quantum sensor. For periods of larger radiation intensity, in which the differences in measurements from both instruments are larger, assimilation of observations with low errors lead to estimates that are closer to the ones obtained by scientific grade sensors. These results suggest that low-cost sensors could be used to obtain inputs for growth models in protected environments, provided there are also imperfect observations of the state.

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Section: Assimilation Of Observationsmentioning

confidence: 99%

Can Accuracy Issues of Low-Cost Sensor Measurements Be Overcome With Data Assimilation?

et al. 2023

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“…Overcoming these challenges requires the application of robust and adaptive management strategies that involve the implementation of technology at all stages of the hierarchical agricultural production system. Greenhouses are prominent facilities that can help to address such challenges [ 2 ]. Greenhouses are designed to protect crops and provide suitable environmental conditions to favor their growth.…”

Section: Introductionmentioning

confidence: 99%

A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network

Guesbaya

García-Mañas

Rodríguez

et al. 2023

Sensors

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In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.

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“…Process-based crop models have been developed and improved to support agricultural decisions on many scales and purposes [ 1 – 4 ]; with the process-based approach, the genotypic, environmental, and management influences on crops can be quantified. Food and feed crop models in open fields are representative process-based crop models [ 2 ], and horticultural crop models in greenhouses are also frequently reported [ 1 , 4 ]. These crop models have been modified and improved for decades by various research groups in various regions for diverse purposes [ 5 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Because of the variation in the crop models, they have become uncoordinated: a modification or an improvement in a crop model is not ensured for the applicability to another model. Regardless of the target crops and scales, studies on the models have redundancy problems in common [ 3 , 4 , 7 , 10 ]. In the decades-long course of the crop modeling progression, the methodology has been torn into pieces due to the differences in objectives and research scales, and the disjunction has resulted in more fragments and redundancy.…”

Section: Introductionmentioning

confidence: 99%

Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders

et al. 2023

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Crop models have been developed for wide research purposes and scales, but they have low compatibility due to the diversity of current modeling studies. Improving model adaptability can lead to model integration. Since deep neural networks have no conventional modeling parameters, diverse input and output combinations are possible depending on model training. Despite these advantages, no process-based crop model has been tested in full deep neural network complexes. The objective of this study was to develop a process-based deep learning model for hydroponic sweet peppers. Attention mechanism and multitask learning were selected to process distinct growth factors from the environment sequence. The algorithms were modified to be suitable for the regression task of growth simulation. Cultivations were conducted twice a year for 2 years in greenhouses. The developed crop model, DeepCrop, recorded the highest modeling efficiency (= 0.76) and the lowest normalized mean squared error (= 0.18) compared to accessible crop models in the evaluation with unseen data. The t-distributed stochastic neighbor embedding distribution and the attention weights supported that DeepCrop could be analyzed in terms of cognitive ability. With the high adaptability of DeepCrop, the developed model can replace the existing crop models as a versatile tool that would reveal entangled agricultural systems with analysis of complicated information.

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Process-based greenhouse climate models: Genealogy, current status, and future directions

Cited by 37 publications

References 120 publications

Can Accuracy Issues of Low-Cost Sensor Measurements Be Overcome With Data Assimilation?

Can Accuracy Issues of Low-Cost Sensor Measurements Be Overcome With Data Assimilation?

A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network

Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders

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