A Sustainable Bio-AI Framework for Agricultural Resource Management Using Arthrobacter crystallopoietes PJC-S08

Abstract Sustainable agricultural production requires technologies that improve nutrient-use efficiency, reduce dependence on synthetic inputs, preserve soil ecological health, and support resilient bio-based resource management. Phosphate-solubilizing microorganisms can transform poorly available soil phosphorus into forms accessible to plants, while artificial intelligence (AI) provides tools for modeling complex relationships among microbial growth, enzyme production, nutritional variables, and environmental conditions. This study evaluates the experimentally characterized alkaliphilic actinomycete Arthrobacter crystallopoietes PJC-S08 and proposes a predictive Bio-AI framework for optimizing its agricultural utility. Isolate PJC-S08, originally designated isolate A7 during screening, showed strong inorganic phosphate-solubilizing activity with a phosphate-solubilization index (PSI) of 3.52 ± 0.07. Molecular identification based on 16S rRNA sequencing showed 98.97% identity with Arthrobacter crystallopoietes ATCC 15481, and phylogenetic analysis using MEGA-7 placed the isolate within the Arthrobacter lineage. Maximum crude alkaline phosphatase (ALP) activity was 43.3 U mL⁻¹ under optimized conditions of 25 °C, pH 9.0, and 96 h incubation. Sequential ammonium sulfate precipitation, gel-filtration chromatography, and ion-exchange chromatography produced a 36.07-fold purification, 52% recovery, and final specific activity of 120.71 U mg⁻¹ proteins. HPLC analysis also confirmed indole-3-acetic acid (IAA) production at 28.45 ± 1.12 μg mL⁻¹. The Bio-AI framework incorporates Multiple Linear Regression, Random Forest, Support Vector Regression, Artificial Neural Network, Gradient Boosting, and XGBoost models to represent non-linear interactions among cultivation temperature, pH, incubation time, carbon and nitrogen sources, inoculum density, and substrate concentration. Importantly, model-generated outputs are treated as testable hypotheses rather than experimental observations. The framework therefore provides a structured pathway from microbial characterization to laboratory optimization, greenhouse evaluation, field validation, and sustainable agricultural resource management. The approach is aligned with SDGs 2, 9, 12, 13, and 15.