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Water pollution is a significant environmental issue that has emerged because of industrial and economic growth. Human activities such as industrial, agricultural, and technological practices have increased the levels of pollutants in the environment, causing harm to both the environment and public health. Dyes and heavy metals are major contributors to water pollution. Organic dyes are a major concern because of their stability in water and their potential to absorb sunlight, increasing the temperature and disrupting the ecological balance. The presence of heavy metals in the production of textile dyes adds to the toxicity of the wastewater. Heavy metals are a global issue that can harm both human health and the environment and are mainly caused by urbanization and industrialization. To address this issue, researchers have focused on developing effective water treatment procedures, including adsorption, precipitation, and filtration. Among these methods, adsorption is a simple, efficient, and cheap method for removing organic dyes from water. Aerogels have shown potential as a promising adsorbent material because of their low density, high porosity, high surface area, low thermal and electrical conductivity, and ability to respond to external stimuli. Biomaterials such as cellulose, starch, chitosan, chitin, carrageenan, and graphene have been extensively studied for the production of sustainable aerogels for water treatment. Cellulose, which is abundant in nature, has received significant attention in recent years. This review highlights the potential of cellulose-based aerogels as a sustainable and efficient material for removing dyes and heavy metals from water during the treatment process.

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. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain.