Abstract
This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images.
Highlights
To help cope with the rapid increase in the human population and future demands on worldwide food security, automation in agriculture is necessary
The algorithm includes the steps of orthographic plant projection based on a perspective transform, plant segmentation using excessive green, plant detection by utilizing projection histograms, and plant counting that compensates for overlapping areas between consecutive images
The system uses an embedded microcontroller mounted on an ATV to receive the odometry signal and trigger the image acquisition
Summary
To help cope with the rapid increase in the human population and future demands on worldwide food security, automation in agriculture is necessary. Algorithm steps in this paper include image sequencing using SIFT (Scale Invariant Feature Transform) feature matching, vegetation segmentation based on color channels, corn plant center detection using a skeletonizing algorithm, and calculation of corn spacing and plant count. This algorithm yields satisfactory results with images captured from the top view. The algorithm includes the steps of orthographic plant projection based on a perspective transform, plant segmentation using excessive green, plant detection by utilizing projection histograms, and plant counting that compensates for overlapping areas between consecutive images (to avoid double-counting) Both the camera and microcontroller were mounted on an ATV (all-terrain vehicle) and the images were analyzed offline. This paper draws to a conclusion and discusses future work in Sections 6 and 7, respectively
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