Machine learning applied to the prediction of citrus production

Abstract

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

Assessment of Olive Tree Canopy Characteristics and Yield Forecast Model Using High Resolution UAV Imagery

“…Also, the usage of high spatial resolution NDVI vegetation index in such models is pioneering because we would be able to have more accurate yield forecast using indices during cultivation period and therefore to intervene with proper cultivation techniques. Other models have been developed with machine learning in order to predict the yield in citrus cultivars, utilizing data such as trees' age, irrigation, and variety for yield estimation instead of remote sensing data [31].…”

Review of machine learning and deep learning models in agriculture

“…A hybrid approach was proposed to perform yield classification of sugarcane based on various soil and climate parameters [66]. A classification model was developed to predict the production in an orchard and determine the effects of ML-based models and factors on production [67]. Two separate artificial intelligence models were developed to predict ET0 (Evapotranspiration) by using only temperature data in Sichuan region of China [68].…”

Monotonicity-based consensus states for the monometric rationalisation of ranking rules with application in decision making

“…In that way, we do not consider here a voting problem where each voter expresses his/her personal preferences on the set of candidates, but an identification problem where the goal is to recognize the unknown true ranking on the set of candidates. For more details, we refer to [45].…”