Dr. D. Shivani, Assistant Professor (Genetics and Plant Breeding), School of Agriculture, Kaveri University, Gowraram (V), Wargal (M), Siddipet – 502279.
Seed quality, particularly genetic purity, is a critical determinant of crop performance and farmer profitability. Conventional methods for assessing purity in seed production fields are labor-intensive, time-consuming, and prone to subjectivity. The Drone Pheno+ project aims to develop an integrated drone–AI phenotyping system for rapid, non-destructive, and high-precision monitoring of genetic purity in seed production of rice, maize, and cotton. The system will employ unmanned aerial vehicles (UAVs) equipped with multispectral/hyperspectral sensors to capture crop canopy images, which will be analyzed using advanced machine learning algorithms to identify varietal and hybrid-specific phenotypic signatures. Optimized flight protocols and AI models will be developed for each crop and validated across diverse hybrids/varieties in multiple agro-climatic zones of India. The expected outcomes include accurate, real-time field purity assessment maps and decision-support tools for seed certification agencies and producers. This technology will enhance the efficiency, accuracy, and scalability of seed quality monitoring, ultimately contributing to improved productivity and profitability in Indian agriculture.
The project aims to develop and validate Drone Pheno+, an integrated UAV–AI system for high-throughput field phenotyping to monitor genetic purity in seed production of rice, maize, and cotton. The system will combine multispectral/hyperspectral imaging sensors mounted on drones with advanced computer vision and machine learning algorithms to detect varietal and hybrid-specific phenotypic traits at different crop growth stages.
In the development phase, optimized flight protocols, image acquisition parameters, and AI-based trait extraction models will be established for each crop. Field trials will be conducted in controlled seed production plots to generate labeled datasets for model training.
In the evaluation phase, the system will be deployed across multiple agro-climatic zones to assess accuracy, robustness, and scalability under variable environmental and management conditions. The outputs will include real-time purity assessment maps, anomaly detection alerts, and decision-support dashboards for seed certification and quality assurance.
The integration of drone-based phenotyping with AI analytics is expected to significantly reduce time, labor, and subjectivity in seed quality monitoring while improving precision and efficiency in large-scale seed production systems.
2 Years
Kaveri Seed Company Ltd. Gowraram (V), Wargal (M), Siddipet Dist. Telangana -502279
Anusandhan National Research Foundation
1,07,21,133 lakhs
Kaveri University was established as per the Telangana Private Universities (Establishment & Regulation) Act, 2018 under section 3.
