Filipe Neves dos Santos

About

Filipe Neves dos Santos is a prominent robotics and computer vision researcher whose work sits at the intersection of artificial intelligence and precision agriculture. His research centers on autonomous agricultural robotics, deep learning-based perception, and robotic navigation, with a particular focus on developing practical solutions for real-world farming environments. Santos has made substantial contributions to the field through his investigations into object detection systems for crops, most notably his highly cited work evaluating SSD and YOLO models for tomato detection in greenhouses (177 citations), alongside benchmark studies comparing deep learning approaches for crop identification. His survey papers on localization, mapping, and path planning for agricultural robots have become essential references for the community, collectively accumulating nearly 300 citations and demonstrating his ability to synthesize complex research landscapes. Particularly noteworthy is his pioneering work on steep slope vineyard robotics, addressing some of the most challenging terrains for autonomous systems, including safety-aware path planning and reliable monitoring platforms. His broader contributions span precision spraying automation, grape bunch detection, and comprehensive reviews of deep learning in agriculture. With over 900 total citations across his most influential works, Santos has established himself as a key figure shaping the future of intelligent, autonomous agricultural systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse

   177 citations · 2021
2. 2
   Localization and Mapping for Robots in Agriculture and Forestry: A Survey

   127 citations · 2020
3. 3
   Path Planning for ground robots in agriculture: a short review

   111 citations · 2020
4. 4
   Deep Learning Applications in Agriculture: A Short Review

   104 citations · 2019
5. 5
   Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato

   88 citations · 2022
6. 6
   Vineyard trunk detection using deep learning – An experimental device benchmark

   62 citations · 2020
7. 7
   Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models

   62 citations · 2021
8. 8
   Path Planning Aware of Robot’s Center of Mass for Steep Slope Vineyards

   54 citations · 2019
9. 9
   Towards a Reliable Robot for Steep Slope Vineyards Monitoring

   53 citations · 2016
10. 10
    Smarter Robotic Sprayer System for Precision Agriculture

    48 citations · 2021

Key Collaborators

Contact & Links