Machine learning applied to the prediction of citrus production

Article English OPEN
Díaz, Irene ; Mazza, Silvia M. ; Combarro, Elías F. ; Giménez, Laura I. ; Gaiad, José E. (2017)
  • Publisher: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
  • Journal: Spanish Journal of Agricultural Research (issn: 2171-9292, eissn: 2171-9292)
  • Related identifiers: doi: 10.5424/sjar/2017152-9090
  • Subject: mandarin | orange | Agriculture | lemon | framework | irrigation | S | lemon; mandarin; orange; M5-Prime; age; framework; irrigation | Agricultural engineering | M5-Prime | age

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.
  • References (37)
    37 references, page 1 of 4

    Agustí M, 2000. Crecimiento y maduración del fruto. In: Fundamentos de Fisiología Vegetal. McGraw Hill, Madrid. 669 pp.

    Agustí M, 2003. Citricultura. Ed. Mundi-Prensa, Madrid. 456 pp.

    Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F, 2016a. An intelligent structural damage detection approach based on self-powered wireless sensor data. Aut Construc 62: 24-44. https://doi.org/10.1016/j.autcon.2015.10.001

    Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F, 2016b. Damage detection using self-powered wireless sensor data: An evolutionary approach. Measurement 82: 254- 283. https://doi.org/10.1016/j.measurement.2015.12.020

    Altman NS, 1992. An introduction to kernel and nearestneighbor nonparametric regression. The Amer Statist 46 (3): 175-185. https://doi.org/10.1080/00031305.1992.10 475879

    Arango RB, Díaz I, Campos AM, Combarro EF, Canas EF, 2015. On the influence of temporal resolution on automatic delimitation using clustering algorithms. Appl Math Inf Sci 9 (2L): 339-347.

    Basak D, Pal S, Patranabis DC, 2007. Support vector regression. Neural information processing. Letters and Reviews 11 (10): 203-224.

    Behnood A, Behnood V, Gharehveran MM, Alyamac KE, 2017. Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Constr Build Mater 142: 199-207. https://doi. org/10.1016/j.conbuildmat.2017.03.061

    Breiman L, 2001. Statistical modeling: The two cultures (with discussion). Statist Sci 16 (3): 199-231. https:// doi.org/10.1214/ss/1009213726

    Das SK, Samui P, Sabat AK, 2011. Application of Artificial Intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil. Geotech Geol Eng 29 (3): 329-342. https://doi.org/10.1007/ s10706-010-9379-4

  • Metrics
    No metrics available