IoT-BASED EVAPOTRANSPIRATION ESTIMATION OF PEANUT PLANT USING DEEP NEURAL NETWORK-Reference-Cited by-同舟云学术

IoT-BASED EVAPOTRANSPIRATION ESTIMATION OF PEANUT PLANT USING DEEP NEURAL NETWORK

Published:2023-08-17 Issue: Volume: Page:487-496
ISSN:2068-2239
Container-title:INMATEH Agricultural Engineering
language:en
Short-container-title:INMATEH

Author:

Suhardi Suhardi¹,Marhaenanto Bambang²,Putra Bayu Taruna Widjaja²,Winarso Sugeng³

Affiliation:

1. Agricultural Engineering, Faculty of Agricultural Technology, University of Jember, Jember, Indonesia 68121, Doctoral study program in agricultural science, Faculty of Agriculture, University of Jember, Jember, Indonesia 68121

2. Agricultural Engineering, Faculty of Agricultural Technology, University of Jember, Jember, Indonesia 68121

3. Doctoral study program in agricultural science, Faculty of Agriculture, University of Jember, Jember, Indonesia 68121

Abstract

The water availability in soil strongly influences crop growth by sustaining photosynthesis, respiration, and the maintenance of plant temperature. The water availability will decrease due to crop evapotranspiration (ETc) which is influenced by reference evapotranspiration (ETo) and crop coefficient (Kc). During water shortage, Kc is strongly influenced by soil evaporation coefficient (Ke) and basal crop coefficient (Kcb) which can be calculated using the Blue Red Vegetation Index (BRVI). The purpose of this study was to apply and evaluate a new method of estimating ETo, Ke, and Kcb at a research site using a Deep Neural Network (DNN) with minimum requirements. The results of the ETo estimation using DNN shows a good output with a determinant coefficient (R2) being 0.774. Meanwhile, the estimates of Ke and Kcb show excellent results with the determinant coefficient (R2) being 0.9496 and 0.999 respectively.

Publisher

INMA Bucharest-Romania

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science

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