Automated Crop Measurements with UAVs: Evaluation of an AI-Driven Platform for Counting and Biometric Analysis

João Victor da Silva Martins (), Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, Wellington Souto Ribeiro and Luan Pereira de Oliveira ()
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João Victor da Silva Martins: Department of Horticulture, University of Georgia, Tifton, GA 31793, USA
Marcelo Rodrigues Barbosa Júnior: Department of Horticulture, University of Georgia, Tifton, GA 31793, USA
Lucas de Azevedo Sales: Department of Horticulture, University of Georgia, Tifton, GA 31793, USA
Regimar Garcia dos Santos: Department of Horticulture, University of Georgia, Tifton, GA 31793, USA
Wellington Souto Ribeiro: Department of Agronomy, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil
Luan Pereira de Oliveira: Department of Horticulture, University of Georgia, Tifton, GA 31793, USA

Agriculture, 2025, vol. 15, issue 21, 1-13

Abstract: Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R 2 = 0.98, error = 11.48%) and canopy area (R 2 = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R 2 of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting.

Keywords: UAV imagery; AI-driven analytics; decision support systems; remote sensing; specialty crops; precision horticulture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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