The growing demand for organically produced vegetables requires the adoption of new cropping systems such as strip-cropping. To counteract the additional labour mixed cropping entails, automation and robotics play a key role. This research focuses on the development of a proof-of-concept platform that combines optical sensors and an actuation system for targeted precision fertilization that encircles selected plants rather than a local field area. Two sensor types are used for the detection of a fertilisation need: a multispectral camera and light detection and ranging (LiDAR) devices in order to acquire information on plant health status and three-dimensional characterisation. Specific algorithms were developed to more accurately detect a change in fertilization need. An analysis of their results yields a prescription map for automatic fertilisation through a robotic arm. The relative location of the platform within the prescription map is essential for the correct application of fertilizers, and is acquired through live comparison of a LiDAR pushbroom with the known 3D world model. The geometry of each single plant is taken into account for the application of the sprayed fertiliser. This resulted in a reliable method for the detection of delayed growth and prototype localization within a changing natural environment without relying on external markers.

This study addresses the challenge of detecting white grape clusters (Vitis vinifera L) in high-density vineyard canopies, a critical task for precision viticulture and yield estimation. Traditional statistical and image-processing methods have struggled with occlusion issues. In this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility. Convolutional Neural Network (CNN) architectures were compared, highlighting YOLOv8 as superior to Mask R-CNN in both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance: 84.9% precision, 72.6% recall, and mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision, 26% recall). The model successfully detected partially hidden clusters, including those invisible to human experts, better than previous studies that required controlled backgrounds or artificial lighting. Results confirm that combining RGB equalization with data augmentation optimizes detection. These findings underscore the potential of deep learning and low-cost RGB imaging systems to enable automated, scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under field conditions, overcoming previous limitations.

This study addresses the challenge of detecting white grape bunches (Vitis vinifera L.) in high-density vineyard canopies, a critical task for precision viticulture and yield estimation. Traditional statistical and image-processing methods have struggled to cope with occlusion issues. In this work, more than 200 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation. Several preprocessing strategies were evaluated to enhance bunch visibility. Different convolutional neural network (CNN) architectures were compared, with YOLOv8 outperforming Mask R-CNN in terms of both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance, with 84.9% precision, 72.6% recall, and an mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision and 26% recall). The model successfully detected partially occluded grape bunches, including some that were not visible to human experts, and outperformed previous studies that relied on controlled backgrounds or artificial lighting. The results demonstrate that combining RGB equalization with data augmentation significantly improves detection performance. These findings highlight the potential of deep learning and low-cost RGB imaging systems to enable automated and scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under real field conditions, effectively overcoming previous technological limitations.