Enabling early identification of nutritional deficiencies in hazelnut orchards through a data-driven robotic framework
Author(s)
Date Issued
2026
Type
article
Volume
246
Start Page
111560
Abstract
Identifying nutritional deficiencies at an early stage is crucial for maximizing yield production and ensuring
healthy plants. Conventional methods generally rely on time-consuming analysis conducted by agronomic
experts. To address this challenge, this study presents a data-driven approach for the early identification of
nutritional deficiencies in hazelnut orchards. Different custom datasets, composed of images acquired in a real
hazelnut orchard as well as in a controlled laboratory environment, are collected, and the performance of five
state-of-the-art machine learning models in early detecting nutritional deficiencies is compared. In particular,
ResNet, DenseNet, MobileNet, EfficientNet, and ConvNext models, along with a baseline based on support
vector machines, are considered. Data augmentation techniques are introduced to synthetically increase the
datasets, and their effectiveness is extensively evaluated. Additionally, a pipeline is designed to carry out the
early identification of nutritional deficiencies onboard an agricultural robot. Experimental results on the early
identification show that ConvNext achieves the highest performance: 81.79% accuracy and 0.8168 F1 score
on a real-world dataset with four classes, and 75.54% accuracy with 0.7552 F1 score for the more challenging
six-class scenario. Furthermore, the effectiveness of the integrated system is validated in preliminary laboratory
experiments using a Turtlebot2 mobile base and a Franka Research 3 arm, equipped with RGB-D cameras.
Subjects