Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing

Efficiently managing agricultural systems necessitates accurate data collection from crops to examine phenotypic characteristics and improve productivity. Traditional data collection processes for specialty horticultural crops are often subjective, labor-intensive, and may not provide accurate information for precise management decisions in phenotypic studies and crop production. Reliable and standardized techniques to record and evaluate crop features using agricultural technology are essential for improving agricultural systems. The objective of the research was to develop a methodology for accurate measurement of blackberry flowers and vegetation coverage using UAV remote sensing and image analysis. The UAV captured 20,812 images in the visible spectrum, and ImageJ software (version 1.54k) was used for segmenting floral and vegetative coverage to calculate variety-specific flower coverage. A moderately strong positive correlation (r = 0.71) was found between flower-to-vegetation ratio (FVR) and visually estimated flower area, validating UAV-derived flower coverage as a reliable method for estimating flower density in blackberries. The regression model explained 51% of the variance in flower estimates (R2 = 0.51), with a root mean square error (RMSE) of 2.79 flower/cm2. Additionally, distinct temporal flowering patterns were observed between primocane- and floricane fruiting blackberries. Vegetative growth also exhibited stability, with strong correlations between consecutive weeks. The temporal analysis provided insight into growth phases and flowering peaks critical for time-sensitive management practices. UAV computer vision for quantifying blackberry phenotypic features is an effective tool and a unique methodology that speeds up the data collection process at high accuracy for breeding research and farm data management.