Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry.
View Article and Find Full Text PDFWastewater treatment activities in the chemical industry have generated abundant gypsum waste, classified as scheduled waste (SW205) under the Environmental Quality Regulations 2005. The waste needs to be disposed into a secure landfill due to the high heavy metals content which is becoming a threat to the environment. Hence, an alternative disposal method was evaluated by recycling the waste into fired clay brick.
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