prediction of wheat production using artificial neural networks and investigating indirect factors affecting it: case study in canterbury province, new zealand

نویسندگان

m. safa

s. samarasinghe

m. nejat

چکیده

an artificial neural network (ann) approach was used to model the wheat production. from an extensive data collection involving 40 farms in canterbury, new zealand, the average wheat production was estimated at 9.9 t ha-1. the final ann model developed was capable of predicting wheat production under different conditions and farming systems using direct and indirect technical factors. after examining more than 140 different factors, 6 factors were selected as influential input into the model. the final ann model can predict wheat production based on farm conditions (wheat area and irrigation frequency), machinery condition (tractor hp ha-1 and number of passes of sprayer) and farm inputs (n and fungicides consumption) in canterbury with an error margin of ±9% (±0.89 t ha-1).

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modelling of Energy Consumption in Wheat Production Using Neural Networks “Case Study in Canterbury Province, New Zealand“

An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year. In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The fin...

متن کامل

Modelling of Energy Consumption in Wheat Production Using Neural Networks “Case Study in Canterbury Province, New Zealand“

An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year. In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The fin...

متن کامل

New Method of Artificial Neural Networks (ANN) in Modeling Broiler Production Energy Index in Alborz Province

During the past few years, modeling in agriculture has attracted considerable attention. New modeling methods including neural networks are employed in various industries, and it is necessary that their use in agriculture be also considered. This research addressed the trend of energy use in broiler farms in Alborz Province and sought to model the trend of energy consumption and production in t...

متن کامل

New Method of Artificial Neural Networks (ANN) in Modeling Broiler Production Energy Index in Alborz Province

During the past few years, modeling in agriculture has attracted considerable attention. New modeling methods including neural networks are employed in various industries, and it is necessary that their use in agriculture be also considered. This research addressed the trend of energy use in broiler farms in Alborz Province and sought to model the trend of energy consumption and production in t...

متن کامل

Uncertainty of Artificial Neural Networks for Daily Evaporation Prediction (Case Study: Rasht and Manjil Stations)

This research uses the multilayer perceptron (MLP) model to predict daily evaporation at two synoptic stations located in Rasht and Manjil, Guilan province, in north-west of Iran. Initially the most important combinations of climatic parameters for both of the stations were identified using the gamma test; and daily evaporation were modeled based on the obtained optimal combination. The results...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید


عنوان ژورنال:
journal of agricultural science and technology

ناشر: tarbiat modares university

ISSN 1680-7073

دوره 17

شماره 4 2015

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023