Browsing by Author "Heyns, Karlien"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Estimation methods for date palm yield : a feasibility study
    (Stellenbosch : Stellenbosch University, 2021-03) Heyns, Karlien; Bekker, J. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: With a growing population and a need for food security, crop yield prediction is vital; not only is it used by exporters and importers, but also by the farmer who needs to plan marketing strategies and determine prices. Methods on crop yield prediction are more abundant for annual plants than for perennials. Very few reliable crop yield prediction models have been developed on the date palm, which is grown in arid regions with plentiful water available. Date fruit is a nutritious food which is produced in many countries and consumed widely around the world. Farming with date palms is a complex process with a large variety of factors affecting the annual yield. This study investigated the feasibility of predicting date yield using data collected by a research partner producing date fruit. Data on some farming practices as well as weather conditions was collected from 2010 onwards, at different levels of detail. Machine learning techniques were considered for prediction of yield; however, four applica- ble linear regression techniques were identified and could be used with the available data for feature selection. The dataset has many features, but dominant features were extracted from the data. Some of the feature selection methods used were a correlation technique, stepwise regression and regularisation. These features were further used to develop regres- sion models. It was found that some weather features were important, as well as features describing the date bunch mass. The latter were observed by sampling bunches from trees in different orchards. Linear regression models were developed on orchard level and on farm level, i.e., for the farm as a whole, and the best-performing linear regression models were selected (while avoiding overfitting). The yield predictions following from these models were compared to the actual annual yield recorded, as well as the estimated yield determined by a rather pragmatic yield prediction method devised by the research partner. The selected models produced a 4% prediction error while the farm method gives a 7% error. The proposed models reduced the prediction error and eliminate the need for laborious sampling work done to support the farm prediction model. The study found that certain data that is collected is not needed by the proposed linear regression models. The study was done from an industrial engineering perspective, and a systematic process was followed to critically assess the data available. This was done to keep complexity of the models at a level suitable for reasonable and accurate yield prediction, and to eliminate some unnecessary data collection labour on the farm.