FSA-GhostNet: A Frequency–Spatial Adaptive Lightweight Network for Deployment-Oriented Crop Growth Monitoring

Timely assessment of crop growth is essential for greenhouse management because irrigation, nutrient supply, pruning, and harvest scheduling all depend on reliable information on plant development. In practice, greenhouse imagery is affected by illumination variation, occlusion, and cluttered backgrounds, whereas deployment-oriented vision models must remain compact enough for resource-constrained computing environments. To address this challenge, FSA-GhostNet was developed as a lightweight visual backbone for greenhouse crop growth monitoring, and a Cucumber Growth Dataset (CGD) was constructed for dense temporal observation of cucumber development. The model was evaluated on ImageNet-100 and CGD under a unified protocol, and a joint learning setting was further used for simultaneous growth-stage classification and continuous Days After Planting (DAP) regression. FSA-GhostNet achieved accuracies of 78.98% on ImageNet-100 and 98.22% on CGD with 2.07 million trainable parameters. Under a unified RTX 3060 runtime setting, the model required 8.08 MB of storage, 0.868 G FLOPs, 13.96 ms/image latency, and 161.58 MB of peak GPU memory. In the joint prediction setting, it achieved 96.03% classification accuracy, a mean absolute error of 0.93 days, a root mean square error of 1.36 days, and an R2 score of 0.9962. These results show that FSA-GhostNet maintains a strong balance between predictive performance and compactness for greenhouse crop monitoring, while CGD provides a useful temporal resource for fine-grained agricultural growth analysis. More broadly, the findings suggest that lightweight agricultural vision models can extend beyond coarse stage recognition toward more continuous and agronomically meaningful monitoring of crop development.