文章信息

標題：CNN feature based graph convolutional network for weed and crop?recognition in smart farming

期刊：《 Computers and Electronics in Agriculture》

第一單位：山東農業大學

在線日期：2020-05-13

Highlights

1.提出了一種基于圖像的半監督學習方法用于雜草和作物識別；

2.在四個不同的雜草數據集上進行了性能評估，準確率高達98.93%，優于傳統的CNN方法；

3.該方法可用于類似的識別任務。

摘要

除草是提高作物產量的有效方法。準確可靠的雜草識別是精準農業實現高精度定點除草的前提。為了提高雜草和農作物識別的準確率，提出了一種基于CNN特征的圖像卷積網絡(GCN)識別方法。基于提取的雜草CNN特征及其歐氏距離，構建了GCN圖。在半監督學習的基礎上，GCN圖通過利用已標記和未標記的圖像特征來豐富模型，測試樣本通過在圖上進行傳播來從已標記的雜草數據中獲取標簽信息。GCN-ResNet-101方法在4個不同的雜草數據集上的識別率分別達到97.80%、99.37%、98.93%和96.51%，優于目前最先進的方法(AlexNet、VGG16和ResNet-101)。此外，該方法的運行時間也滿足了田間雜草控制的實時性要求。本文提出的基于CNN特征的GCN方法有利于在有限的標簽數據下進行多類農作物和雜草的識別，在處理類似的農業識別任務中具有應用潛力。此外，所使用的數據集和源代碼是公開的，以便于在田間雜草識別方面的研究。

圖3.?基于CNN特征的GCN用于雜草和作物識別流程

Abstract

Weeding is an effective way to increase crop yields. Reliable and accurate weed recognition is a prerequisite for achieving high-precision site-specific weed control in precision agriculture. To improve weed and crop recognition accuracy, a CNN feature based graph convolutional network (GCN) based approach is proposed. A GCN graph was constructed based on extracted weed CNN features and their Euclidean distances. Based on the semi-supervised learning, the GCN graph enriched the model by exploiting labeled and unlabeled image features, and testing samples obtain label information from labeled weed data by performing propagation over the graph. The proposed GCN-ResNet-101 approach achieved 97.80%, 99.37%, 98.93% and 96.51% recognition accuracies on four different weed datasets respectively, which outperformed the state-of-the-art methods (AlexNet, VGG16 and ResNet-101). Additionally, the runtime of the proposed approach also satisfies the real-time requirement of field weed control. The proposed CNN feature based GCN approach is favorable for multi-class crops and weeds recognition with limited labeled data, which is a promising approach in dealing with similar agricultural recognition tasks. Furthermore, the used datasets and source code are publicly available to facilitate the research in the recognition of field weeds.