A review on weed detection using ground-based machine vision and image processing techniques

By Aichen Wang, Wen Zhang, Xinhua Wei. Computers and Electronics in Agriculture, 2019

590 citations recorded by the source corpus.

Abstract Weeds are among the major factors that could harm crop yield. With the advances in electronic and information technologies, machine vision combined with image processing techniques has become a promising tool for precise real-time weed and crop detection in the field, providing valuable sensing information for site-specific weed management. This review summarized the advances of weed detection using ground-based machine vision and image processing techniques. Concretely, the four procedures, i.e., pre-processing, segmentation, feature extraction and classification, for weed detection were presented in detail. To separate vegetation from background, different color indices and classification approaches like color index-based, threshold-based and learning-based ones, were developed. The difficulty of weed detection lies in discriminating between crops and weeds that often have similar properties. Generally, four categories of features, i.e., biological morphology, spectral features, visual textures and spatial contexts, were used for the task, which were discussed in this review. Application of conventional machine learning-based and recently developed deep learning-based approaches for weed detection were also presented. Finally, challenges and solutions provided by researchers for weed detection in the field, including occlusion and overlap of leaves, varying lighting conditions and different growth stages, were discussed.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/bb6f12d363338698aeae3b397c7333eb3c003e4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Arpit Jain
2. Dionysis D. Bochtis
3. Abhinav Sharma
4. Prateek Gupta
5. V. Chowdary

In Ecology

1. Arpit Jain
2. Dionysis D. Bochtis
3. Abhinav Sharma
4. Prateek Gupta
5. V. Chowdary

In Plant Science

1. Arpit Jain
2. Dionysis D. Bochtis
3. Abhinav Sharma
4. Aristotelis C. Tagarakis
5. Prateek Gupta
6. V. Chowdary

Citations in the corpus

• A Survey of Deep Learning Techniques for Weed Detection from Images (2021, background): “A. Wang et al. (2019) highlighted that the most challenging part of a weed detection techniques is to distinguish between weed and crop species.”
• Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, background): “In [96]–[99] author has reviewed machine and deep learning techniques for weed, pests and disease identification, and classification in crops at different growth stages.”
• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “The majority of these review studies have been dedicated to crop disease detection [13–16], weed detection [17,18], yield prediction [19,20], crop recognition [21,22], water management [23,24], animal welfare [25,26], and livestock production [27,28].”