A critical review on the influence of humidity for plant growth forecasting

Chia, S Y; Lim, Mee Wei

doi:10.1088/1757-899x/1257/1/012001

Cited by 29 publications

(10 citation statements)

References 15 publications

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“…A Study on the relative influence of humidity on Lactuca Sativa "suggests that the optimal relative humidity that could improve plant growth is 85±2%. As low humidity below optimum level will increase stomatal resistance" (Chia and Lim, 2022). The resolution used here is not higher that 1%, which is consistent with other studies (Shibata et al, 1995;Vanhassel et al, 2015).…”

Section: Relative Humiditysupporting

confidence: 90%

Development of a Hydroponics Simulator to Generate Guidelines for Data Collection in Hydroponics for Machine Learning Applications

Swiatek,

Kuthe,

Al-Shrouf

et al. 2023

Agriculture & Food

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Hydroponics, a soilless growing method using nutrient-rich water solutions, offers distinct advantages in plant growth and resource efficiency. Data-driven optimization in hydroponics relies on precise control of environmental factors. To effectively apply data-driven optimization, comprehensive and accurate data describing hydroponic plant growth is essential. In the first part of the project and in this paper, we seek to identify critical parameters that influence plant growth, recommend optimal resolutions and measurement frequencies, and address challenges in data collection. Standardization of data collection procedures and establishment of data collection guidelines are fundamental to obtaining high quality, comparable and reproducible data. Establishing a standard for data collection will allow data from different experiments to be compared and used in future research. In the initial phase of the project and throughout this paper, our primary objective is to identify the relevant parameters affecting plant growth in a hydroponic system. These critical parameters encompass aspects such as light intensity, spectral composition, nutrient solution composition, water quality, pH levels, and more. By conducting a thorough investigation, we aim to recommend optimal resolutions for these parameters. Additionally, we will address the challenges associated with collecting data related to these key variables, ensuring that the data obtained is reliable and useful to further research and scientific exploration in the field of hydroponics. Accurate data on these characteristics enables data-driven decisions and methods, such as machine learning and machine vision, which in turn allow optimization of hydroponic productivity. The study conducted a comprehensive analysis and identified 30 essential parameters crucial for characterizing a hydroponic system. The findings are summarized as a list of relevant parameters that need to be measured or recorded to enable data-driven optimization of hydroponic systems.

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Section: Relative Humiditysupporting

confidence: 90%

Development of a Hydroponics Simulator to Generate Guidelines for Data Collection in Hydroponics for Machine Learning Applications

Swiatek,

Kuthe,

Al-Shrouf

et al. 2023

Agriculture & Food

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“…The relative humidity of the ambient air is an important factor for normal photosynthesis and plant growth. Maintaining high humidity in the hydroponic system reduces the transpiration coefficient in the cultivated lettuce and improves plant growth [30]. On the other hand, the increase in humidity increases the risk of the development of disease-causing pathogens.…”

Section: Resultsmentioning

confidence: 99%

Design of a Small-Scale Hydroponic System for Indoor Farming of Leafy Vegetables

et al. 2023

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The reduction of the population and labour force in rural areas, adverse climate changes, soil pollution and degradation, and reduced soil fertility necessitate new and intensive approaches to and methods of vegetable production. In this study, a new high-tech, small-scale hydroponic system based on the Internet of Things (IoT) for growing leafy vegetables was designed. For the assembly of the high-tech small-scale hydroponic system, low-cost materials and sensors are used, allowing remote monitoring and process automation during the cultivation of leafy vegetables and seedlings. Experimental investigations of the installation were conducted, with lettuce as the grown crop. The environmental and technological parameters of the system were monitored and analysed in order to assess the suitability of the system. The obtained results confirm that the small-scale hydroponic system maintained the set parameters of air temperature, air humidity and pH of the nutrient solution within the optimal limits for growing lettuce. Other parameters, such as temperature and electrical conductivity of the nutrient solution, had small deviations from the required optimal limits. In the initially set experiment, 75% germination of the lettuce seeds was achieved. The main advantages of the proposed hydroponic system are the simplicity of its management and easy installation. Furthermore, it offers the possibility for remote monitoring and control without any high requirements towards the experience of the user with such facilities. The designed and implemented small hydroponic system can help small and medium-sized vegetable growers achieve indoor sustainable farming of leafy vegetables year-round.

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“…It promotes the plant growth and grain yield. High RH conditions limits the VPD and increases the stomatal conductance, and promotes diffusion of CO 2 inside the stomata while balancing the transpiration rate resulting into high photosynthetic rates and pronounced vegetative growth 30 .…”

Section: Discussionmentioning

confidence: 99%

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.)

Nataraj,

Gupta,

Singh

et al. 2024

Sci Rep

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Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years’ data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations—Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

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A critical review on the influence of humidity for plant growth forecasting

Cited by 29 publications

References 15 publications

Development of a Hydroponics Simulator to Generate Guidelines for Data Collection in Hydroponics for Machine Learning Applications

Development of a Hydroponics Simulator to Generate Guidelines for Data Collection in Hydroponics for Machine Learning Applications

Design of a Small-Scale Hydroponic System for Indoor Farming of Leafy Vegetables

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.)

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