Document Type

Article

Publication Date

2024

DOI

10.3390/land13101585

Publication Title

Land

Volume

13

Issue

10

Pages

1585

Abstract

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields.

Rights

© 2024 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The data presented in this study are available at https://www.kaggle.com/datasets/vbookshelf/v2-plant-seedlings-dataset (accessed on 27 May 2024); https://github.com/zhangchuanyin/weed-datasets (accessed on 25 June 2024)."

Original Publication Citation

Hu, W., Chen, T., Lan, C., Liu, S., & Yin, L. (2024). SkipResNet: Crop and weed recognition based on the improved ResNet. Land, 13(10), 1-21, Article 1585. https://doi.org/10.3390/land13101585

ORCID

0000-0002-8040-0367 (Liu)

Download

Share

COinS