Study on Grey-Markov method and its application in agricultural production forecast

Zhou, Yan; Yang, Xusheng; Wang, L.R.

doi:10.1109/gsis.2007.4443335

Cited by 1 publication

(1 citation statement)

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“…These machine learning methods have already been successfully used in precision agriculture tasks. For instance, Yu et al 26 proposed a crop yield forecasting model based on the combination of artificial neural networks (ANNs) while, in 27 , Zhou et al applied the Grey-Markov forecasting model 28 to yield prediction. Saruta et al 29 found that predictive models using support vector machines had the potential to describe the relationship between yield or protein content and multiple explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Using regression trees to predict citrus load balancing accuracy and costs

Bóbeda¹,

Combarro²,

Mazza³

et al. 2018

IJCIS

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In order to define management and marketing strategies, farmers need adequate knowledge about future yield with the greatest possible accuracy and anticipation. In citrus orchards, greater variability and non-normality of yield distributions complicate the early estimation of fruit production. This study was conducted with the objective of developing a method to estimate citrus load based on orchard characteristics, morphological information of trees and number of fruits in defined locations of the crow. Field data from 16 citrus orchards obtained from 2005/06 through 2013/14 seasons were used. Machine learning techniques were applied to predict yield; these methods can reduce the estimation error as well as decrease the need for in-field measuring, thus reducing both the cost and time of the process.

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Section: Introductionmentioning

confidence: 99%