用CNN识别农田杂草，准确率达94%。
Weed Detection using Convolutional Neural Network
- 用预训练模型提取图像特征，微调用于杂草检测
- 在15336个图像片段上实现94%的检测准确率
- 适合农业自动化除草系统开发者参考
本文利用卷积神经网络（CNN）进行农田杂草检测，重点研究Conv2d与空洞Conv2d两种卷积层在作物田间杂草识别中的应用。所提方法通过预训练模型提取输入图像特征，并针对杂草检测任务进行微调。实验基于包含15336个图像片段的大规模数据集，其中土壤占3249个，大豆占7376个，草类占3520个，阔叶杂草占1191个。结果表明，该方法可实现94%的检测准确率，显著提升杂草管理效率，有助于减少有毒除草剂使用，对智慧农业具有重要意义。
原文摘要 · Abstract (English)
In this paper we use convolutional neural networks (CNNs) for weed detection in agricultural land. We specifically investigate the application of two CNN layer types, Conv2d and dilated Conv2d, for weed detection in crop fields. The suggested method extracts features from the input photos using pre-trained models, which are subsequently adjusted for weed detection. The findings of the experiment, which used a sizable collection of dataset consisting of 15336 segments, being 3249 of soil, 7376 of soybean, 3520 grass and 1191 of broadleaf weeds. show that the suggested approach can accurately and successfully detect weeds at an accuracy of 94%. This study has significant ramifications for lowering the usage of toxic herbicides and increasing the effectiveness of weed management in agriculture.
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