Automatic detection of crop rows in maize fields with high weeds pressure

Abstract

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu's method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Automatic expert system based on images for accuracy crop row detection in maize fields

“…They used linear regression in each of three crop row segments considered and a cost function analogous to the moment of the best-fit line to detect lines fitted to outliers (i.e., noise and weeds) as a means of identifying row guidance information. Montalvo et al (2012) apply a linear regression for crop row detection in images containing high weeds densities. Some templates are used to guide the detection.…”

“…An important issue related with the application of machine vision methods is that concerning the crop row and weed detection, which has attracted numerous studies in this area (Burgos-Artizzu, Ribeiro, Tellaeche, Pajares, & Fernández-Quintanilla, 2009;López-Granados, 2011;Montalvo et al, 2012;Onyango & Marchant, 2003;Sainz-Costa, Ribeiro, Burgos-Artizzu, Guijarro, & Pajares, 2011;Tellaeche, Burgos-Artizzu, Pajares, Ribeiro, & Fernández-Quintanilla, 2008). The goal is to eliminate weeds to favor the growth of crops.…”

An opinion on imaging challenges in phenotyping field crops

“…Compared to the problem of identifying phenotypes from archival images, this simpler goal has already received considerable attention [12][13][14][15][16]. Figure 6 illustrates two situations in which such algorithms might be applied.…”

“…In a uniformly planted field, many parameters can be measured "disembodied and in bulk": the average values of the greens for chlorophyll content; or the average position of the blue/green sky/plant boundary for plant height; or the average deviation of a leaf from the vertical for leaf angle [11][12][13][14][15][16]. Traditional assessments of crop health by aerial and satellite vehicles rely on existing techniques, which are now being applied to ground-based images in production situations.…”

Visually-Aided Localisation for Autonomous Agricultural Vehicles

“…They applied a naive Bayesian classifier and a Gaussian mixture clustering algorithm to discriminate weeds from crop. Montalvo et al (2012) performed a double thresholding operation on the excess green image using Otsu's method (Otsu et al 1975), to first separate green plants from soil, then subsequently to separate crop and weeds, under the assumption that crops will have a different "greenness" than the weeds. Burgos-Artizzu et al (2011) suppressed weeds by applying a logical AND operation to consecutive green-segmented and motionstabilised frames, under the assumption that crop rows form continuous lines, while weeds in the inter-row are scattered.…”