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

Engenharia Agrícola

OdÍlio C. Da Rocha Neto¹  Adunias Dos S. Teixeira¹  Arthur P. De S. Braga¹  Clemilson C. Dos Santos¹  Raimundo A. De O. LeÃo¹ 

关键词: precision irrigation;    automation;    neural algorithms;    irrigação de precisão;    automação;    algoritmos neurais;   

DOI  :  10.1590/1809-4430-Eng.Agríc.v35n2p266-279/2015

来源: SciELO

【 摘 要 】

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

【 授权许可】

CC BY   
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