Intellectual Property India Publishes Patent Application for Agro-Climatic Adaptive Edge-Deployed Orchard Intelligence System With Harvest Timing Optimization

MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202611064632 A) filed by Skuast Kashmir Rbic Foundation Rbic on May 22, 2026, for Agro-Climatic Adaptive Edge-Deployed Orchard Intelligence System With Harvest Timing Optimization.

Inventor includes Sahil Mushtaq.

The application for the patent was published on July 17, 2026, under issue no. 29/2026.

Abstract: ABSTRACT Agro-Climatic Adaptive Edge-Deployed Orchard Intelligence System with Harvest Timing Optimization The present invention discloses AgroSyn-X Orchard Edition, an edge-deployed, agro-climatic adaptive orchard intelligence system designed to enhance fruit tree resilience, improve fruit quality, and optimize harvest timing under drought and heat stress. The system integrates a climate-resilient microbial consortium, an intelligent adaptive encapsulation matrix, an edge AI optimization engine with TinyML, a uni-fied soil-microbe-tree digital twin model, and an explainable artificial intelligence framework into a closed-loop, self-learning bio- digital platform.The microbial con-sortium comprises dynamically optimized ratios of drought-tolerant rhizobacteria, nitrogen-fixing strains, phosphate-solubilizing microorganisms, ACC deaminase-producing bacteria, and heat-resistant endospore-forming microbes. This consortium is protected and delivered via a biodegradable intelligent adaptive encapsulation ma-trix that provides controlled, environment-triggered release based on real-time soil moisture, temperature, and phenological thresholds.The edge AI engine with Ti- nyML processes smartphone-captured data locally to optimize microbial formula-tions, encapsulation parameters, and harvest predictions in real time, even offline. The unified digital twin model simulates soil-microbe-tree interactions for accurate forecasting, while the explainable AI framework delivers transparent recommenda-tions and confidence scores. The system continuously learns through reinforcement learning, making it highly suitable for small and marginal orchard farmers.

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