Agricultural crop–weed visual models are commonly evaluated within a single dataset, whereas practical model selection requires evidence across datasets, annotation policies, image conditions, and inference constraints. We present a reproducible reliability benchmark for weed-presence classification, crop/weed detection, and semantic segmentation using CropAndWeed and PhenoBench as primary datasets, with CWFID and CWD30 providing external diagnostic evaluation. The study combines an auditable, task-aligned annotation-harmonization protocol with bidirectional transfer and retention analysis, calibration and selective prediction, controlled image-condition stress, uncertainty, seed stability, subgroup analysis, and batch-1 inference reference. Evaluation across these dimensions revealed direction-dependent transfer and separated models with similar source-domain performance according to confidence quality and image-condition sensitivity. In a representative detection setting, a source-domain mAP50–95 of 0.669 corresponded to a target-domain score of 0.409, equivalent to 61.1% retention, while Gaussian blur radius 7 reduced the source-domain score to 0.298. Post-hoc temperature scaling improved ECE and Brier score in 8 of 12 classification cases. External diagnostics further identified visual, annotation, and task differences that materially affect transfer. The resulting evidence base supports agricultural model selection using transfer behavior, confidence quality, stress sensitivity, and computational cost alongside source-domain accuracy.
The integration of autonomous driving technologies into tractors marks a major step forward in modern farming, improving efficiency and precision. However, slippery terrains like paddy fields present challenges that require reliable path-following algorithms to handle uncertainties and nonlinear dynamics. In this study, an enhanced path following control methodology is proposed through the integration of feedforward compensation with model predictive control (FFC-MPC) for autonomous tractors operating in low-adhesion paddy fields. The proposed algorithm integrates soil-induced lateral slip effects to adjust the wheel steering angles, with the objective of minimizing trajectory tracking errors. To evaluate the performance of the proposed algorithm, comparative assessments were conducted within a multi-body dynamic simulation framework. Additionally, field tests were performed to evaluate the performance of the algorithm in the real-world operating conditions. The results demonstrate that, during straight-line trajectory tracking, the proposed FFC-MPC algorithm achieves a 64.6% reduction in tracking error relative to the model-independent pure pursuit control (PPC) algorithm and a 35.5% reduction relative to the model-based Linear Quadratic Regulator (LQR) approach. In more complex scenarios involving curved and transitional path segments, the FFC-MPC algorithm achieves an 86.8% reduction in tracking error compared to PPC and a 55.8% relative to LQR. The performance enhancements improve the efficiency and stability of autonomous tractors in paddy fields and show potential for broader application in other high-slip environments.
