YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting

Accurate detection of tomato growth stages is essential for stage-specific greenhouse management and precision agriculture. In Bhutan, greenhouse cultivation is affected by altitude variability, large diurnal temperature fluctuations, diffuse illumination, limited automation, and a scarcity of locally annotated datasets, limiting the applicability of conventional deep learning models. This work proposes Pheno-Lite + Efficient Channel Attention (ECA), a lightweight, phenology-aware object detection architecture derived from Ultralytics YOLOv5 for tomato growth stage recognition. A balanced dataset of 2,464 annotated images was constructed from locally collected greenhouse images in Bhutan and publicly available tomato images, with augmentation designed to simulate local greenhouse conditions. The dataset includes vegetative (820), flowering (824), fruiting (820), and background (26) samples. The proposed architecture introduces two customized backbone modules: C3 PhenoLite, which enhances spatial and texture feature extraction using depthwise residual refinement, and C3 ECA, which strengthens inter-channel feature interactions through efficient channel attention. The proposed model achieves 90.6% precision, 88.8% recall, and 92.6% mAP@50, with 4.0 million parameters and 10.9 GFLOPs at 640 x 640 resolution. These results demonstrate its potential for real-time and climate-resilient greenhouse deployment in Bhutan.

Automating robotic harvesting in intensive agriculture within Mediterranean greenhouses requires overcoming significant challenges related to the geometric complexity of plants and occluded fruits. Although existing literature offers solutions targeting crops that grow in isolation (e.g., apples, sweet peppers, or peaches), the fundamental challenge lies in cluster-growing vegetables, where fixed sensors mounted on robotic systems fail to detect fruits hidden behind the visible surface. To address this limitation, this study presents a comprehensive pipeline for the 3D reconstruction and precise localization of each fruit within a cluster, including heavily occluded instances. The proposed methodology is structured into five sequential stages: i) point cloud acquisition using the AgriSEE Next Best View (NBV) active planner; ii) stochastic noise filtering via Statistical Outlier Removal (SOR); iii) surface classification and segmentation using Region Growing (RG); iv) isolation and recovery of occluded fruits through Density-Based Spatial Clustering of Applications with Noise (DBSCAN); and v) 3D pose estimation (position and orientation). This approach extracts the complete cluster geometry, ensuring the reliable identification of partially hidden tomatoes. Evaluated across multiple scenarios with varying occlusion levels within a simulation framework rigorously validated against real-world conditions, the system achieves a precision exceeding 90%, an average recall of 82.8%, and a mean Intersection over Union (mIoU) of 80.7%. Furthermore, it demonstrates high repeatability in centroid estimation with a Root Mean Square Error (RMSE) of merely 4.2~mm, verifying its technical feasibility and high accuracy for autonomous harvesting operations.

In this manuscript we release two datasets for visual sensing of tomato plants grown in commercial-like settings and acquired using a robot. The first is BUTom21 which consists of still images and manual annotations. The second is BUTom-ST21 which consists of video-based data and semi-automated annotations through AI-based methods, referred to as pseudo-labels. In both cases, we provide pixel-level labels for the ripeness of the fruit. The aim is to provide the research community a challenging set of real-world imagery to explore methods to sense and estimate the state of tomato plants and their fruit, which is an important horticultural crop. Importantly, the spatial-temporal dataset provides individual fruit count and ripeness information enabling researchers to push the boundaries of field-based phenotyping.