Alexandra Boini

About

Alexandra Boini is a leading researcher in precision horticulture and agricultural robotics, with a primary focus on developing real-time, computer-vision systems for fruit detection. Her work bridges the gap between classical hard-coded feature extraction algorithms and modern deep-learning approaches, directly addressing the computational bottlenecks that have historically prevented automated fruit detection from being viable for in-field, real-time applications. Boini’s most influential contribution is her 2019 paper on "Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree," which has garnered 165 citations. This work demonstrated that single-shot CNN architectures could achieve high detection accuracy while dramatically reducing processing time, making them suitable for integration with robotic harvesters and yield estimation systems. She further advanced the field by systematically comparing deep-learning networks against classical algorithms in her 2020 study, providing a critical benchmark for the agricultural vision community. Boini’s research is foundational for the next generation of smart farming technologies, enabling faster, more reliable, and computationally efficient fruit detection that directly impacts labor efficiency and crop management.

Research Focus

Key Achievements

Top Papers

1. 1
   Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree

   165 citations · 2019
2. 2
   Comparing deep-learning networks for apple fruit detection to classical hard-coded algorithms

   3 citations · 2020

Key Collaborators

Contact & Links