###### Abstract  
Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system. rice nutrient level; fertilizer optimization; nutrient analysis; polynomial regression; nutrient prediction; environmental impact reduction +
Footnote †: journal: Medical  
and on-site manual tasks [6; 7]. The deficiency of vital nutrients can lead to reduced crop yields [8; 9; 10; 11; 12; 13]. This empowerment enables prompt action in response to anticipated or unexpected deviations such as crop nutrient concentration and allows for the simulation of the effects of interventions such as nutrient recovery based on real-life data [14; 15; 16; 17; 18]. In this context, the application of machine learning (ML) offers a promising avenue for farmers. ML equips them with tools for monitoring soil quality and delivering personalized recommendations, drawing insights from both experimental and field data. Nonetheless, the prediction of rice essential nutrients remains a formidable challenge, primarily due to several factors: (1) the inherent variability in nutrient content, (2) the diversity of analytical approaches, (3) limitations in data availability, (4) genetic diversity among rice varieties, and (5) the associated cost and time constraints [16; 17; 18; 19]. Consequently, it is imperative to address these multifaceted challenges to develop accurate and reliable nutrient prediction models for rice [15; 16; 17]. This paper report one of our digital twin case studies on rice nutrient recovery through two approaches; namely, single-nutrient concentration prediction and nutrient composition concentration prediction. Regression facilitates the identification of intricate relationships among essential rice nutrients, ensuring their optimal supply, thereby enhancing rice growth and nutrient content [20; 21]. This study seeks to identify the most effective regression algorithm for predicting nutrient concentration percentages based on the co-existence and composition of other nutrients. The incorporation of regression algorithms in the crop digital twin is mainly because of its efficiency and effectiveness. This endeavor promises optimized nutrient management practices, culminating in enhanced rice quality and a reduced environmental footprint through the adjustment of nutrient ratios.