Deep learning techniques for in-crop weed recognition in large-scale grain production systems: a review

2021/03/27 by Kun Hu, Zhiyong Wang, Guy Coleman +7 · 1 voice · 71 citations
Agricultural and Biological Sciences · Computer Science · Engineering · Mathematics · #Agricultural engineering #Agricultural productivity #Agriculture #Agronomy #Artificial intelligence #Computer science #Crop productivity #Data science #Date Palm Research Studies #Deep learning #Engineering #Field (mathematics) #Geography #Mathematics #Plant Disease Management Techniques #Precision agriculture #Productivity #Smart Agriculture and AI #Weed #Weed control #cs.CV

paper · pdf · doi:10.1007/s11119-023-10073-1

published in Precision Agriculture 25(1), 1-29 (Springer Science+Business Media)

arxiv published 2021/03/27 · openalex publication_date 2023/09/22 · openalex created_date 2023/09/23 · arxiv updated 2024/02/05 · openalex updated_date 2026/07/23

Abstract

Abstract Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem.

Citations

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Spectral Networks and Locally Connected Networks on Graphs
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning in Neural Networks: An Overview
• ImageNet classification with deep convolutional neural networks
• SSD: Single Shot MultiBox Detector
• A Public Image Database for Benchmark of Plant Seedling Classification Algorithms
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal\n Networks
• A Survey of Model Compression and Acceleration for Deep Neural Networks
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
• Recent advances in convolutional neural networks
• Generalization and Representational Limits of Graph Neural Networks
• Explainable Deep Learning: A Field Guide for the Uninitiated
• Towards practical object detection for weed spraying in precision agriculture

Discussions

• Deep Learning Techniques for In-Crop Weed Identification: A Review Authors: Kun Hu, Zhiyong Wang, Guy Coleman, Asher Bender, Tingting Yao, Shan Zeng, Dezhen Song, Arnold Schumann, Michael Walsh abs: h [mastodon, 0 points, 0 comments]

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