However,thecompression only in the spatial domain suffers from a dramatic performance drop without finetuning and is not robust to noise, as the noise in the spatial domain can easily confuse the pruning criteria, leading to some parameters/channels being pruned incorrectly.

Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels.

DNN pruning eliminates redundant weight parameters without compromising representation power.

Hence, a desirable pruning algorithm should achieve high accuracy and accelerate inference for various types of networks without significant training overheads in costs and complexity.

Deploying deep learning models in agriculture is difficult because edge devices have limited resources, but this work presents a compressed version of EcoWeedNet using structured channel pruning, quantization-aware training (QAT), and acceleration with NVIDIA's TensorRT on the Jetson Orin Nano. Despite the challenges of pruning complex architectures with residual shortcuts, attention mechanisms, concatenations, and CSP blocks, the model size was reduced by up to 68.5% and computations by 3.2 GFLOPs, while inference speed reached 184 FPS at FP16, 28.7% faster than the baseline. On the CottonWeedDet12 dataset, the pruned EcoWeedNet with a 39.5% pruning ratio outperformed YOLO11n and YOLO12n (with only 20% pruning), achieving 83.7% precision, 77.5% recall, and 85.9% mAP50, proving it to be both efficient and effective for precision agriculture.

Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in various fields. However, these advancements have led to a substantial increase in the complexity and size of these networks. This poses a challenge when deploying large and complex networks on edge devices. Consequently, model compression has emerged as a research field aimed at reducing the size and complexity of CNNs. One prominent technique in model compression is model pruning. This paper will present a new technique of pruning that combines both channel and layer pruning in what is called a "hybrid pruning framework". Inspired by EfficientNet, a renowned CNN architecture known for scaling up networks from both channel and layer perspectives, this hybrid approach applies the same principles but in reverse, where it scales down the network through pruning. Experiments on the hybrid approach demonstrated a notable decrease in the overall complexity of the model, with only a minimal reduction in accuracy compared to the baseline model. This complexity reduction translates into reduced latency when deploying the pruned models on an NVIDIA JETSON TX2 embedded AI device.