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

Abstract

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil-Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product-moment correlation coefficient.

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

“…In order to improve the speed and effectively of HT, the algorithm with gradient-based Random Hough Transform (RHT) (Xu and Oja, 1993) was applied by Ji and Qi (2011) to detect the centre lines of crop rows. THor methods based on TH were widely used in the crop Science Publications AJABS row detection, meanwhile, many others methods were developed and each of them has the advantages and deficiencies (Guerrero et al, 2013;Jiang et al, 2010;Montalvo et al, 2012;Romeo et al, 2012).…”

Fleets of robots for environmentally-safe pest control in agriculture

“…These methods are described in Guerrero et al (2013). In this specific case, the crop row positions were determined with respect to the UGV for UGV guiding and weed detection purposes.…”

“…The first is inspired by the Hough transformation and is detailed in Romeo et al (2013). The second applies linear regression based on the robust Theil-Sen estimator, detailed in Guerrero et al (2013), where green plants are segmented by the thresholding method proposed in Montalvo et al (2013). An inertial measurement unit (IMU) provides information regarding external camera parameters, pitch (a) and roll (h), so that, along with all other parameters (both internal and external), four predictable crop rows are identified, serving as the basis for identifying the four real lines through the two methods mentioned above.…”

Plant Protection Robot Row-type Guidance Technique Based on Laser Radar

“…From a robotics perspective, all these applications can be enabled with a relatively simple yet challenging capability: determination of position and orientation [1][2]. Using the method of machine vision has certain limitation in row-type guidance such as light conditions and atmospheric effects [3].…”