Estimating the leaf area index of bell peppers according to growth stage using ray-tracing simulation and a long short-term memory algorithm

Lee, Joon Woo; Kang, Woo Hyun; Moon, Taewon; Hwang, In Kyeom; Kim, Dongpil; Son, Jung Eek

doi:10.1007/s13580-019-00214-9

Cited by 15 publications

(5 citation statements)

References 47 publications

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“…A seedling with proper hypocotyl length and high compactness is considered as a highquality seedling [17,18]. LAI is used to characterize canopy light condition and photosynthetic rate [19] and LAR indicates overall leafiness [9], and it is helpful to identify the factors affecting plant growth.…”

Section: The Lues Of Cucumber Scions and Rootstocks As Affected By Air Temperature Light Intensity And Photoperiod Conditions In A Pfalmentioning

confidence: 99%

Evaluation of Air Temperature, Photoperiod and Light Intensity Conditions to Produce Cucumber Scions and Rootstocks in a Plant Factory with Artificial Lighting

Hwang

Chun

et al. 2021

Horticulturae

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Air temperature and light conditions are important factors not only to produce high-quality seedlings but also to promote energy efficiency in a plant factory with artificial lighting. In this study, we conducted two experiments in order to investigate the favorable conditions of air temperature, light intensity and photoperiod for the production of cucumber scions and rootstocks in a plant factory with artificial lighting. Cucumber scions and rootstocks were cultivated in two combined treatments: the combination of three different levels of difference between the day and night temperature (DIF), 25/20, 26/18 and 27/16 °C and five different light intensity conditions of photosynthetic photon flux, 50, 100, 150, 200 and 250 μmol·m−2·s−1 was set for the first experiment, and the combination of three different photoperiod conditions, 12, 16 and 20 h·d−1 and five different light intensity conditions, 50, 100, 150, 200 and 250 μmol·m−2·s−1 was set for the second experiment. In the air temperature and light intensity treatments, the hypocotyl elongation of cucumber scions and rootstocks was affected more largely by light intensity than DIF. The highest DIF treatment (27/16 °C) affected negatively on the accumulation of dry mass. On the contrary, the smallest DIF treatment (25/20 °C) was favorable for seedling growth due to lesser stress by rapid change of air temperature between photo- and dark-period. In the photoperiod and light intensity treatments, an increased DLI (daily light integral) promoted the growth of scions and rootstocks. Under the same DLI condition, the growth of scions and rootstocks increased with increasing photoperiod and decreasing light intensity. In both of experiments, while the dry weight increased with increasing the light intensity, the light use efficiencies were reduced by increasing the light intensity. Considering the growth and quality of seedlings and energy efficiency, the optimal environment conditions were represented by 25/20 °C of air temperature, 150 μmol·m−2·s−1 of light intensity and 16 h·d−1 of photoperiod.

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Section: The Lues Of Cucumber Scions and Rootstocks As Affected By Air Temperature Light Intensity And Photoperiod Conditions In A Pfalmentioning

confidence: 99%

Evaluation of Air Temperature, Photoperiod and Light Intensity Conditions to Produce Cucumber Scions and Rootstocks in a Plant Factory with Artificial Lighting

Hwang

Chun

et al. 2021

Horticulturae

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“…Nevertheless, machine learning models are increasingly being used within the field of horticulture, especially in the form of time-serial deep neural networks (Ali and Hassanein 2020;Jung et al 2020). More recent work also applies these to crop growth prediction (Lee et al 2020;Alhnaity et al 2020).…”

Section: Background and Related Workmentioning

confidence: 99%

Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation

Van Bokkem,

Van den Hemel,

Dumančić

et al. 2023

AAAI

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Increasing global food demand, accompanied by the limited number of expert growers, brings the need for more sustainable and efficient horticulture. The controlled environment of greenhouses enable data collection and precise control. For optimally controlling the greenhouse climate, a grower not only looks at crop production, but rather aims at maximising the profit. However this is a complex, long term optimisation task. In this paper, Constraint Programming (CP) is applied to task of optimal greenhouse economic control, by leveraging a learned greenhouse climate model through a CP embedding. In collaboration with an industrial partner, we demonstrate how to model the greenhouse climate with an LSTM model, embed this LSTM into a CP optimisation framework, and optimise the expected profit of the grower. This data-to-decision pipeline is being integrated into a decision support system for multiple greenhouses in the Netherlands.

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“…Results indicated a potential for more accurate price forecasting one month in advance. Similarly, the ability to forecast rice pest attacks using bidirectional LSTMs with multivariate time-series input was compared to that of vanilla and stacked LSTM 24 . Overall, only a few research studies have utilized LSTM to predict the characteristics of crop growth.…”

Section: Introductionmentioning

confidence: 99%

In-Season Growth Forecasting in Cotton Using Unmanned Aerial System- based Canopy Attributes and LSTM Models

Dhal

Bhandari

Gadepally

et al. 2023

Preprint

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Cotton (Gossypium spp.) is one of the important cash crops in the United States. Monitoring in-season growth metrics, from early season growth to harvest, is crucial for predictive and prescriptive cotton farming. In recent years, forecasting models have garnered considerable attention to predict canopy indices. This allows selection of management options during crop growth to boost cotton yield and profitability. Here, we used unmanned aerial system-derived canopy features, including canopy cover, canopy height, and excess green index, collected from 3500 plots at Driscoll in Corpus Christi, Texas during the years 2019, 2020, and 2021 for in-season growth forecasting. Training datasets in our model were produced by K-Means clustering and Dynamic Time Warping (DTW) techniques were used to compare various Long Short-Term Memory (LSTM) models in predicting the three canopy features. Accuracy was determined using Root Mean Square Error (RMSE). Results indicated higher predictive capacity of Convolutional Neural Networks (CNN) LSTM for canopy cover, and multi-layer stacked LSTMs for canopy height and excess green index respectively. Overall, results show tremendous potential for in-season growth forecasting and management of agricultural inputs like pesticides and fertilizers for improving crop health and productivity.

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Estimating the leaf area index of bell peppers according to growth stage using ray-tracing simulation and a long short-term memory algorithm

Cited by 15 publications

References 47 publications

Evaluation of Air Temperature, Photoperiod and Light Intensity Conditions to Produce Cucumber Scions and Rootstocks in a Plant Factory with Artificial Lighting

Evaluation of Air Temperature, Photoperiod and Light Intensity Conditions to Produce Cucumber Scions and Rootstocks in a Plant Factory with Artificial Lighting

Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation

In-Season Growth Forecasting in Cotton Using Unmanned Aerial System- based Canopy Attributes and LSTM Models

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