Evolution of machine learning in biosciences: A bibliometric network analysis

Vanaja, Akkinepally; Yella, Venkata Rajesh

doi:10.7324/jabb.2022.100505

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…ML is the division of study that gives the ability of computers to learn from huge amounts of data [14]. These ML and deep learning are contemporary technologies that can be used efficiently to predict the water requirements for agricultural crops.…”

Section: Estimation Of Gsr and Et Using ML Modelsmentioning

confidence: 99%

Crop water management using machine learning-based evapotranspiration estimation

Meenal,

Jala,

Samundeswari

et al. 2024

J App Biol Biotech

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variations, the pre-developed systems have not used all available weather data and so no robust models. As a consequence of cost and difficulties in direct measurement techniques with a pyranometer and lysimeter, solar radiation and ET 0 were predicted using suitable models [8]. Different empirical models have been developed for ET 0 estimation rendering to various climatic conditions [9,10]. Many models such as empirical, artificial neural network, machine learning (ML), and deep learning exist in the literature to compute the global solar radiation (GSR) and ET 0 [11,12]. However, the standard method recommended by the Food and Agriculture Organization (FAO), namely, the Penman-Monteith (PM-FAO56) equation requires an extensive range of data support for ET estimation. In this work, ML models with a limited number of input parameters are utilized to estimate the solar radiation and ET in chosen locations of Tamil Nādu. To estimate the ET 0 , the solar radiation and temperature values are used. Empirical correlations are also utilized to estimate the ET 0 for comparison with ML methods. Based on the performance metrics, ML-based ET 0 estimations are more accurate than empirical-based estimations. To develop the ML model, SVM and random forest algorithms are employed with a reduced number of meteorological parameters. STUDY AREA AND DATA SOURCESThe study site, Coimbatore has a semi-arid tropical climate. The tomato is one of the horticultural products produced in the study location. The water requirement for tomato plant in the study location is calculated by the relation of crop coefficient and ET. The geographical parameters of the study location are given in Table 1 and Figure 1 shows the monthly

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Section: Estimation Of Gsr and Et Using ML Modelsmentioning

confidence: 99%