Leveraging artificial intelligence for monitoring microbial interactions in smart agriculture

The integration of artificial intelligence (AI) with microbial ecology offers novel opportunities to enhance the sustainability and productivity of modern agriculture. This study investigates how AI-driven platforms can be applied to monitor microbial interactions in smart agricultural systems, with a focus on soil health, crop resilience, and disease risk prediction. Soil and rhizosphere samples from diversified cropping systems were analyzed using high-throughput sequencing, and environmental metadata were collected through IoT-based sensors. Machine learning and deep learning models, including random forest, support vector machines, and convolutional neural networks, were trained to classify microbial patterns and predict plant disease outcomes. Results demonstrated significantly higher microbial diversity in legume and mixed rotations compared to cereal monocropping, while beta-diversity analyses revealed clear separation of microbial communities shaped by cropping practices and soil moisture. AI models achieved high predictive accuracy, with the convolutional neural network outperforming conventional algorithms, highlighting its capacity to capture complex ecological patterns. Predictor analysis identified both abiotic factors, such as soil moisture and pH, and microbial taxa, including Bacillus, Pseudomonas, and Streptomyces, as key determinants of crop health outcomes. Co-occurrence networks further revealed antagonistic interactions between beneficial microbes and pathogens, underscoring the potential for bio-based disease suppression. These findings confirm the hypothesis that AI-enabled monitoring systems can reliably detect microbial networks, predict disease risks, and inform precision interventions. Practical recommendations include promoting diversified cropping, adopting AI-driven monitoring systems, fostering beneficial microbial consortia through biofertilizers, and developing accessible digital platforms for farmers. The study concludes that merging AI with microbial ecology establishes a proactive framework for precision bio-management, balancing soil health with sustainable crop production and reducing dependency on chemical inputs in an era of global food security challenges.