With artificial intelligence to promote the rapid development of precision agriculture, the management and detection of agricultural resources through computer vision and deep learning is particularly important. Content-Based image classification has produced extensive research, however relative research is still only at the theoretical level. We therefore put forward a high-performance, low-cost, powerful applicability and very feasible scheme to solve many problems which image classification is facing today in agricultural automation. Firstly, we use a fine-tuning strategy to train models to better performance in classification tasks. Secondly, we utilize neural network pruning techniques to reduce neural network size and computational cost, and subsequently retrain models by knowledge distillation to minimize pruned model performance loss. Thirdly, converting trained models to an available ONNX format, thus simplifying the process from theory to practice. And finally, we deploy and inference models in a high-performance deep learning inference platform with C++, that is, NCNN. We conducted experiments on three representative agricultural image classification datasets, PlantVillage, DeepWeeds and Flowers102. The results demonstrate the effectiveness of our scheme.

随着人工智能推动精准农业的快速发展，通过计算机视觉和深度学习对农业资源进行管理和检测显得尤为重要。基于内容的图像分类已经产生了广泛的研究，但相关研究还仅仅停留在理论层面。因此，我们提出了一种高性能、低成本、适用性强且非常可行的方案来解决当今农业自动化中图像分类面临的许多问题。首先，我们使用微调策略来训练模型以在分类任务中获得更好的性能。其次，我们利用神经网络修剪技术来减少神经网络的大小和计算成本，随后通过知识蒸馏重新训练模型，以最大限度地减少修剪后的模型性能损失。第三，将训练好的模型转换为可用的 ONNX 格式，从而简化从理论到实践的过程。最后，我们在使用 C++ 的高性能深度学习推理平台（即 NCNN）中部署和推理模型。我们对三个具有代表性的农业图像分类数据集进行了实验，PlantVillage、DeepWeeds 和 Flowers102。结果证明了我们方案的有效性。