Integrating genomics & AI for precision crop monitoring and adaptive stress management

Abstract

Advancements in genomics and artificial intelligence are transforming precision agriculture by enabling early stress detection and adaptive crop management. Integrating genomic analysis, image-based stress detection, and real-time environmental monitoring, this approach assesses plant responses to stress factors such as drought and disease. A BERT-based model processes genomic data, while computer vision identifies visual stress indicators like wilting and discoloration. IoT sensors track environmental parameters such as soil moisture, temperature, and humidity, refining predictions and optimizing intervention strategies. The system leverages multimodal data fusion to enhance decision-making, improving the accuracy of stress detection and mitigation strategies. Machine learning models continuously adapt by learning from historical and real-time data, making recommendations more precise over time. A web-based platform allows users to upload plant images and environmental data for real-time analysis, generating personalized recommendations for irrigation, fertilization, and disease management. The platform's intuitive interface ensures accessibility for farmers and agricultural experts, facilitating widespread adoption. By combining AI, genomics, and IoT, this system enhances crop health, maximizes yield, and promotes sustainable farming through proactive, data-driven decision-making. Ultimately, it aims to reduce resource waste, mitigate crop losses, and support scalable, technology-driven agricultural solutions.

Code and data availability

The supplied blocks describe a proposed BERT-AgriFusion multimodal plant-stress model and its experiments, but contain no public phenotype/trait dataset, plant image or sensor data deposit, author analysis code, trained model checkpoint, or supplement with such assets. No data or code availability statement with an URL

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