Brunella Morandi

About

Brunella Morandi is a leading researcher at the intersection of precision horticulture and computer vision, whose work is transforming how we monitor and manage fruit crops. Her primary research areas include fruit growth physiology, non-destructive sensing, and the application of artificial intelligence to agricultural robotics. Morandi’s most significant contribution lies in pioneering real-time fruit detection systems for tree canopies. Her landmark 2019 paper, "Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree," which has garnered 165 citations, demonstrated that deep learning could dramatically outperform traditional, computationally intensive algorithms, enabling faster and more accurate detection for automated harvesting and yield estimation. This work bridges the gap between plant physiology and practical agri-tech, as seen in her studies on the relationship between fruit anatomical features and growth strategies. By validating deep-learning networks against classical methods in her 2020 follow-up, Morandi has established a new standard for precision agriculture, making her a key figure in the push toward fully automated, data-driven orchard 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
   From fruit anatomical features to fruit growth strategy: is there a relationship?

   7 citations · 2016
3. 3
   Comparing deep-learning networks for apple fruit detection to classical hard-coded algorithms

   3 citations · 2020

Key Collaborators

Contact & Links