Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Yanlei Xu,Chenxiao Li,Run He,Zongmei Gao,Yubin Jiao,Yuting Zhai

doi:10.3390/agronomy10010113

Yanlei Xu, Chenxiao Li + Show 4 more

Open Access

https://doi.org/10.3390/agronomy10010113

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Abstract

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

Highlights

Field weeds usually grow in inter-row crops, which compete with crops for water, nutrients, and space
For improving the real-time performance of weed identification, this study proposed a rapid objectives of this study were: method to detect weed density based on absolute feature corner point (AFCP) algorithm
The identification accuracy and speed were of great significance, since they determined the possibility of the algorithm being operated in the real-time variable spraying system

Summary

Introduction

Field weeds usually grow in inter-row crops, which compete with crops for water, nutrients, and space. Seriously affecting crop yield and quality [1]. Herbicides are widely used to prevent weed effects; excessive herbicides may cause serious environmental pollution and herbicide waste. To address the issue, weed distribution based variable spraying technology was developed for the precise application rate, with the purpose of achieving minimal farmland pollution and safety of agricultural products, as well as sustainable development of agriculture. The efficiency and accuracy in weed identification in farmland determine the performance of the spraying technique. The conventional methods for identification of weeds mainly use two attributes between the weeds and crops, i.e., the spatial features and location

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