Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Neto, Odílio Coimbra da Rocha; Teixeira, Adunias dos Santos; Braga, Anna Rafaela Cavalcante; Santos, C.; Leão, Raimundo Alípio de Oliveira

doi:10.1590/1809-4430-eng.agric.v35n2p266-279/2015

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…They concluded that an ANN is an efficient method and can be a substitute for the volumetric balance method. Further, they reported that the ANN method has ease of implementation and speed of response when doing the computational analysis (Rocha Neto et al, 2015). Umair and Usman (2010) used ANN technology to study effective irrigation scheduling.…”

Section: Introductionmentioning

confidence: 99%

“…An artificial neural network (ANN) is another modern approach used in different regions of the world to manage water resources. In a study conducted by Rocha Neto et al (2015), an ANN was used to estimate the irrigation period for a watermelon crop. They concluded that an ANN is an efficient method and can be a substitute for the volumetric balance method.…”

Section: Introductionmentioning

confidence: 99%

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Viewpoint: Irrigation water management in a space age

Hafeez¹,

Awan²

2022

Irrigation and Drainage

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Climate change and rapid population growth are already putting increasing demand and pressure on the world's freshwater resources. Irrigated agriculture is responsible for about 70% of global freshwater withdrawals, consuming the most amount of water. However, the diverted water in irrigation systems is often not utilized efficiently because of poor water management at different spatial scales, resulting in a system efficiency of only 30-50% in most Asian countries. Typically, improving water management in irrigated areas requires accurate information on various water balance parameters while also considering a changing climate across different spatial scales. There have been technical limitations in getting accurate and reliable information on various key water balance parameters with the conventional approaches used in the recent past. In the twenty-first century, considerable advances have been made in using satellite imagery, including processing and geospatial algorithms, to estimate hydro-meteorological fluxes and relevant components at different spatial scales. This paper provides a perspective on the application of innovative and non-conventional approaches to water resources management in the Murray Darling basin, Australia, the Indus basin, Pakistan and the Amu Darya basin, Uzbekistan. Examples of the state-of-the-art tools described in this paper include: (i) using geoinformatics to monitor the diagnostic and operational performance of large irrigation schemes; (ii) quantifying groundwater and surface water to better manage these two resources using geoinformatics; (iii) forecasting irrigation supply and demand at high spatial and temporal scales using hydrological modelling based on the nodal network; (iv) forecasting crop yield production by satellite remote sensing. The approaches in this study clearly demonstrate that new monitoring and planning tools and methods are highly effective in improving irrigation water management in the 'space age' (for the purposes of this paper, space age refers to a period in which earth observation satellites are available to accurately monitor agricultural practices and water balance parameters such as soil moisture and evapotranspiration). The application of these innovative tools can assist in strategizing, diagnosing, monitoring and improving the performance of Article title in French: Point de vue: la gestion de l'eau d'irrigation à l'ère spatial.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Viewpoint: Irrigation water management in a space age

Hafeez¹,

Awan²

2022

Irrigation and Drainage

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“…Ensuring a certain degree of interpretability is as important as accuracy so it could be understood by any person if needed, to accomplish that aim the use of an intuitive Mamdani fuzzy inference systems (configured by multiple conditional rule sets) could be a suitable solution [7,8]. Neural networking, usually used in black box systems, is a technology that also contributes to the study of the dynamic behavior of climate; in the agricultural field, its application eases the water use balance in irrigation [9,10]. Furthermore, computer models capable of predicting climate behavior and the impact of greenhouse gases on earth's temperature were developed some decades ago, however, these models only analyze the CO2 related variables [11], presence, and concentration, when research has shown there are many others involved [12].…”

Section: Introductionmentioning

confidence: 99%

Environment humidity and temperature prediction in agriculture using Mamdani inference systems

Velandia

Quintana

Vanegas-Ayala

2021

IJECE

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<span>This paper presents the results of a humidity and temperature prediction model in the environment for agriculture, using diffuse sets and optimizing their parameters by heuristic methods, such as genetic algorithms, and exact methods such as Quasi-Newton. It has been identified that non-specialized users could have difficulties in understanding the system dynamics and the behavior of variables over time. The aim of this research is obtain models with a high level of interpretability and accuracy that allows predicting the temperature and humidity values for the environment. The use of fuzzy logic to present a solution has great advantages as this system is highly rated for interpretability. Furthermore, by relating the obtained values for environment humidity and temperature to qualitative categories as high, medium or low, it allows non-specialized users to have a better understanding of the system dynamics. Two optimization techniques are applied to two different diffuse sets that allow the prediction of the humidity and temperature. It is found that the best implementation involves a Mamdani fuzzy inference system optimized with Quasi-Newton algorithm that uses a set of initial values attained through a previous optimization process with a genetic algorithm.</span>

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“…In this context, the application of computer Engenharia Agrícola, Jaboticabal, v.41, n.4, p.418-426, jul./aug. 2021 modeling has been widely used, such as fuzzy logic and neural networks, both in agriculture (Rocha Neto et al, 2015, Putti et al, 2017a, Putti et al, 2017b and in the areas of animal comfort (Schiassi et al, 2015, Hernández Julio et al, 2015, Mirzaee-Ghalehv et al, 2015, Bahuti, et al, 2018, Lourençoni, et al, 2019a, Lourençoni, et al, 2019b, Abreu et al, 2020.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Modeling of the Ocular Temperature of Cattle in Thermal Stress Conditions

et al. 2021

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This study proposes a fuzzy logic model capable of predicting the ocular temperature (OT) of beef cattle by means of infrared thermography. The goal of this study is to establish a methodology for making decisions related to animal welfare. The experiment was carried out at a commercial beef production farm, located in the south of Minas Gerais state, where twenty-eight Brahman cattle (Bos Taurus Indicus) raised in extensive production systems were evaluated. Thermal images of the entire head of the animal were collected in order to measure the ocular temperature (OT). Concurrently, the variables air dry bulb temperature (DBT) and relative humidity (RH) were recorded. The fuzzy logic model was developed using the Mandani inference method, based on the input variables DBT and RH and the output variable OT, and using the experimental data as reference. The proposed fuzzy logic system allows the estimation of the ocular temperature of beef cattle with an error of 1.71% and a coefficient of determination R² of 0.8749. These values validate the proposed fuzzy logic system for helping to make decisions for better animal welfare.

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Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Cited by 13 publications

References 8 publications

Viewpoint: Irrigation water management in a space age

Viewpoint: Irrigation water management in a space age

Environment humidity and temperature prediction in agriculture using Mamdani inference systems

Fuzzy Logic Modeling of the Ocular Temperature of Cattle in Thermal Stress Conditions

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