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Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e. evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning. [ABSTRACT FROM AUTHOR]

Precision agriculture manages within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. Delineation of field-specific management zones (MZs), representing significantly different yield potentials prescribe the rates of a specific crop inputs within-field. This paper examines multiple-year maize grain yield maps (2014, 2015, 2017 and 2018) and their spatial and temporal variability of within-field datasets (soil electrical conductivity [EC], soil organic matter [OM], and elevation) and climate data. The research was undertaken on a non-irrigated field at New Zealand's Foundation for Arable Research (FAR) in the Waikato region, to provide a simple, heuristic method to delineate dynamic MZs for crop inputs. Supervised statistical learning models (stepwise multiple linear regression [SMLR], feedforward neural network (FFNN), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost) and Cubist regression) were implemented to predict spatial yield. Prediction accuracies of the trained models were evaluated by withholding one subset of data for testing. For internal 'split-sample' validation, CART, random forest and XGBoost produced slightly better statistical predictions (RMSE = 1.9–2.0 and R2 = 0.60–0.63) than Cubist and FFNN (RMSE = 2.1–2.2 and R2 = 0.52–0.57), whereas MLR produced the weakest prediction (RMSE = 2.3 and R2 = 0.51). Spatial yield prediction of individual years, were poor (R2 = 0.07–0.36). Input data used is readily and inexpensive for small arable fields in New Zealand. The methods presented, could be applied to a wider range of arable crops for within-field management inputs, to respond to spatially diverse soil texture distribution and variable rainfall patterns. [ABSTRACT FROM AUTHOR]