Prediction Of Oil Palm Yield For Smallholders Estates In Tropical Region Using Extra Trees Method

Khan, Nuzhat (2023) Prediction Of Oil Palm Yield For Smallholders Estates In Tropical Region Using Extra Trees Method. PhD thesis, Universiti Sains Malaysia.

Download (539kB) | Preview


Global food security and sustainable use of natural resources heavily relies on the timely prediction of crop yields. The oil palm, being the most profitable crop for oil production worldwide, requires accurate yield predictions to maintain a balance between its global demand and supply. This thesis proposes machine learning regression approach to predict oil palm fresh fruit bunches yield. The main objective of this research is to develop a machine learning model trained on actual data to predict long-term oil palm yield with high precision. The study utilizes data obtained from multiple sources including Malaysia Palm Oil Board (MPOB), Meteorological Department Malaysia (MET) and NASA. The proposed methodology is implemented on site specific data recorded from an entire state Pahang Malaysia. The data is comprised of 18 variables including historical yield, soil, and weather variables, to accurately predict future yield. The statistical analysis facilitated to assess data quality and to extract the agricultural information. The outcomes of the correlation analysis reveal the complex interdependencies of yield influencing factors. The data exploration is followed by a preprocessing pipeline to convert raw data into meaningful information. The data preprocessing pipeline includes treating outliers, normalization, features selection and data splitting into training, testing, and validation sets. Based on the prepared data, the automated model selection is used to identify the most appropriate prediction model.

Item Type: Thesis (PhD)
Subjects: T Technology > TS Manufactures > TS1-2301 Manufactures
Divisions: Pusat Pengajian Teknologi Industri (School of Industrial Technology) > Thesis
Depositing User: Mr Mohammad Harish Sabri
Date Deposited: 27 Feb 2024 08:24
Last Modified: 27 Feb 2024 08:24

Actions (login required)

View Item View Item