AgriEngineering

This study addresses the challenge of instance segmentation for key grapevine structures (Trunk, Branch, and Bud) in complex natural environments, focusing on issues such as varying light conditions, weather, and significant scale variations. We propose an enhanced instance segmentation model named CDA-YOLOv8. Trained on a self-built dataset of 2160 images covering grapevine scenes under diverse lighting conditions, this model integrates three key components: the ACmix module for enhanced global feature modeling, the C2f-DWR module for optimized multi-scale feature extraction, and the CSPPC module for achieving model lightweighting. We evaluate performance using precision/recall and mAP@50, together with the stricter mAP@[50:95], for segmentation quality, and parameters/model size/FPS for deployment efficiency. Experimental results demonstrate that CDA-YOLOv8 achieves 70.1% precision, 74.4% recall, 76.3% mAP@50, and 36.8% mAP@[50:95], with only 3.19 million parameters and a compact model size of 6.49 MB. Compared with the original YOLOv8-seg, CDA-YOLOv8 improves segmentation accuracy while maintaining high efficiency (6.87 FPS). It also delivers better mask quality under stricter overlap criteria, providing quantitative evidence for real-time perception in automated grapevine pruning systems.

Accurate forecasting of crop maturity supports efficient harvest planning and accelerates selection decisions in breeding programs. In spring wheat, maturity is typically assessed through manual scoring late in the season, which limits its usefulness for timely harvest management and early selection decisions in breeding programs. This study evaluated uncrewed aerial vehicle (UAV)–based multispectral imagery for forecasting maturity in spring wheat grown at Lethbridge, Alberta (AB), Canada, during the 2024 and 2025 growing seasons. Thirty cultivars were monitored using seven-band UAV multispectral imagery during grain filling, enabling derivation of core vegetation and senescence-related indices from radiometrically calibrated orthomosaics. Strong correlations ( ) were observed between vegetation indices and days remaining to maturity (DRTM), motivating baseline regression models and subsequent evaluation of eleven machine-learning and deep-learning approaches. Among these, support vector regression (SVR) and multi-layer perceptron (MLP) achieved the highest predictive accuracy ($R^2=0.95$–$0.96$; mean absolute error (MAE) $\approx 1.25$ days). Deep learning models achieved performance comparable to machine-learning approaches; however, incorporating spatial information through convolutional neural networks did not improve prediction accuracy. Feature-attribution analysis identified the red, red-edge (RE), and near-infrared (NIR) spectral bands as key predictors, enabling non-destructive, early, and scalable UAV-based maturity forecasting.

Whole-plant maize (corn) (WPC) is a critical forage in ruminant diets, and rapid, reliable measurement of its nutritional composition is essential for precision feeding. We hypothesized that an on-site near-infrared spectroscopy (OS-NIRS—specifically, HarvestLab™ 3000) sensor would provide within-laboratory repeatability comparable to commercial analytical laboratories (ALs) and inter-laboratory reproducibility similar to conventional laboratory analyses. To test this, WPC samples were collected across three experiments and two countries (USA and Germany) and analyzed by both OS-NIRS and ALs, with precision metrics calculated according to ISO 5725. Results showed that OS-NIRS achieved intra-laboratory repeatability equal to or greater than ALs, particularly for protein and starch. The repeatability performance of the OS-NIRS sensors was similar to that of ALs for moisture and NDF. Inter-laboratory reproducibility varied widely across constituents and experiments. Including OS-NIRS data with AL measurements produced inconsistent effects—sometimes narrowing confidence intervals but more often widening them—while OS-NIRS data alone demonstrated repeatability on par with ALs but mixed reproducibility outcomes. Inclusion of OS-NIRS data did not introduce systematic bias and, in some cases, improved consistency. These findings indicate that OS-NIRS can complement laboratory analyses by providing timely, farm-level measurements that enhance decision-making in feed management.