Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

Abstract

To realize quick localization of plant maize, a new real-time localization approach is proposed for maize cores at the seedling stage, which can meet the basic demands for localization and quantitative fertilization in precision agriculture and reduce environmental pollution and the use of chemical fertilizers. In the first stage, by taking pictures of maize at the seedling stage in a field with a monocular camera, the maize is segmented from the weed background of the picture. And then the three most-effective methods (i.e., minimum cross entropy, ISODATA, and the Otsu algorithm) are found from six common segmentation algorithms after comparing the accuracy rate of extracting maize and the time efficiency of segmentation. In the second stage, plant core from segmented maize image is recognized, and localized, based on different brightness of the rest part of maize core and plant. Then the geometric center of maize core is considered as localization point. the best effect of extracting maize core was found from the minimum cross entropy method based on gray level. According to experimental validation using many field pictures, under weedy conditions on sunny days, the proposed method has a minimum recognition rate of 88.37% for maize cores and is more robust at excluding weeds.

Application of an improved watershed algorithm based on distance map reconstruction in bean image segmentation

“…The kernel of rape ( Brassica campestris L.) obtained by selective limit corrosion is used as a marker to limit the number of watershed zones and impose a forced minimum on the original gradient image [ 15 ]. The kernel count of adhesion corn ( Zea mays L.) kernel superimposed image can restrain the oversegmentation degree of watershed algorithm effectively [ 16 ]. The adaptive radius circle template is used to detect the corner of the adhesion of rice ( Oryza sativa L.) image.…”

Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

“…To develop the proposed tasks, it is essential to have a good localisation. Conventional methods use GPS [19,20], inertial sensors [21][22][23][24], cameras [25][26][27][28], odometry [29,30], lidars [31][32][33][34][35], kinect [36,37], or combined systems [36,38,39]. Other specific sensors highly used in agriculture are multi-spectral ones, although their application focuses more on the recognition and vegetative analysis than on the location within an environment [40][41][42].…”