Machine Learning and Oil Palm Plantation Management featured in COMPUTERS AND ELECTRONICS IN AGRICULTURE

COMPUTERS AND ELECTRONICS IN AGRICULTURE is a journal that provides international coverage of advances in the development and application of computer hardware, software and electronic instrumentation and control systems for solving problems in agriculture and related industries.

This article is extracted and shared with permission from Elsevier:
Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: A proof of concept analysis. Computers and Electronics in Agriculture, Vol. 151, August 2018, p. 338-348. Copyright © 2018 Elsevier B.V.

Abstract:
Bayesian networks were used to predict yield functions from three commercial oil palm estates. The networks were trained using a range of environmental, agronomic and management data routinely collected during plantation management. The Bayesian networks predicted fruit yield (FFB), average weight of fruit bunches (ABW) and average bunch number per hectare (BUNCH_HA). Comparing the predictions of most probable yield against observed data showed the Bayesian networks were highly accurate, with r2 values between 0.6 and 0.9. Predictions for attaining specific yield targets exceeded 75% accuracy for the FFB, 85% for the BUNCH_HA, and 90% for the ABW function. Supplementary analysis compared the precision of the Bayesian networks with artificial neural networks (ANNs), and demonstrated that the Bayesian networks gave equivalent or superior accuracy for every test. The utility of the networks were demonstrated by predicting the probability of achieving above average yield functions for each block across the three estates using a set of hypothetical rainfall and fertiliser input scenarios during the year prior to harvest. For the majority of blocks, the probability of exceeding the yield target depended on the level of fertiliser and rainfall inputs received, indicating that production from these blocks is greatly influenced by prior rainfall and fertilizer. However, some blocks in favourable areas showed a very high probability of exceeding the mean yields at all rainfall and fertiliser inputs, while a number of other blocks showed a consistently low probability of achieving the same productivity; production from these blocks will be resistant to the effects of historic rainfall and fertiliser inputs. The ability of Bayesian networks to represent future yield expectations will greatly assist managers under pressure to improve the economic and environmental sustainability of plantations. The demonstration that machine learning can extract important insight from complex datasets will have broad application in the analysis of big data collected from oil palm as well as other agricultural industries.

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