Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques
The grain moisture content at harvest time is a key factor that limits harvesting windows. The present study aimed to develop a new methodology to predict wheat moisture content by using multi-layer perceptron (MLP) and support vector regression (SVR) techniques. Five input variables included the nu...
Main Authors: | Shamsollah Abdollahpour, Armaghan Kosari-Moghaddam, Mohammad Bannayan |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2020-12-01
|
Series: | Information Processing in Agriculture |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317319301696 |
Similar Items
-
Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture
by: Arief Andy Soebroto, et al.
Published: (2022-04-01) -
Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR
by: Saba Mirza Alipour, et al.
Published: (2021-10-01) -
Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing
by: Ming-Der Yang, et al.
Published: (2021-08-01) -
Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents
by: Andre Loechte, et al.
Published: (2021-12-01) -
Assessment of the harvesting costs of different combine harvester fleets
by: Jüri Olt, et al.
Published: (2019-03-01)