José Miguel Guerrero

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

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 Otsus 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.

Support Vector Machines for crop/weeds identification in maize fields

In Precision Agriculture (PA) automatic image segmentation for plant identification is an important issue to be addressed. Emerging technologies in optical imaging sensors play an important role in PA. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, are applied for weeds elimination. Maize is an irrigated crop, also unprotected from rainfall. After a strong rain, soil materials (particularly clays) mixed with water impregnate the vegetative cover. The green spectral component associated to the plants is masked by the dominant red spectral component coming from soil materials. This makes methods based on the greenness identification fail under such situations. We propose a new method based on Support Vector Machines for identifying plants with green spectral components masked and unmasked. The method is also valid for post-treatment evaluation, where loss of greenness in weeds is identified with the effectiveness of the treatment and in crops with damage or masking. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.

Automatic expert system for weeds/crops identification in images from maize fields

Automation for the identification of plants, based on imaging sensors, in agricultural crops represents an important challenge. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, can be applied for weeds elimination. This requires the identification of weeds and crop plants. Sometimes these plants appear impregnated by materials coming from the soil (particularly clays). This appears when the field is irrigated or after rain, particularly when the water falls with some force. This makes traditional approaches based on images greenness identification fail under such situations. Indeed, most pixels belonging to plants, but impregnated, are misidentified as soil pixels because they have lost their natural greenness. This loss of greenness also occurs after treatment when weeds have begun the process of death. To correctly identify all plants, independently of the loss of greenness, we design an automatic expert system based on image segmentation procedures. The performance of this method is verified favorably.

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

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.

Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images

In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensors positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects.

Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous) for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a) spectral bands (visible and infrared); (b) imaging sensors and optical systems (including intrinsic parameters) and (c) geometric visual system arrangement (considering extrinsic parameters and stereovision systems). A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management) project for effective weed control in maize fields (wide-rows crops), funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.

Curved and straight crop row detection by accumulation of green pixels from images in maize fields

This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.

Identification of Plant Textures in Agricultural Images by Principal Component Analysis

In precision agriculture the extraction of green parts is a very important task. One of the biggest issues, when it comes to computer vision, is image segmentation, which has motivated the research conducted in this work. Our goal is the segmentation of vegetative and soil parts in the images. For this proposal a novel method of segmentation is defined in which different vegetation indices are calculated and through the reduction of components by principal component analysis (PCA) we obtain an enhanced greyscale image. Finally, by Otsu thresholding, we binarize the grayscale image isolating the green parts from the other elements in the image.

Crop rows and weeds detection in maize fields applying a computer vision system based on geometry

Display Omitted We design a method to guide a tractor correctly at the starting point in the field.We determine weeds density applying the system geometry.We control the overlapping of the treated areas based on a GPS.We design an automatic method to guide the tractor in real-time during the treatment. This paper proposes a new machine vision system, focused on crop row detection with three main goals: (1) to achieve a correct orientation at the starting point in the field for each path; (2) to achieve a precise guidance during path following and (3) to determine the weed density and overlapping. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to turns, vibrations and undesired movements. The images are captured under image perspective projection, being affected by the above undesired effects. To achieve the above goals, we have designed an image processing strategy comprising two main modules: (a) crop rows detection with precise adjustment and (b) data extraction for determining weeds densities for site specific-treatments, guidance and the overlapping of the area where the treatment is to be applied. Both modules form a full process, and each module contains different processes, some of them coming from existing approaches (image segmentation and crop row estimation with adjustment) and other specifically designed for the proposed approach (exposure time control, position and orientation of the tractor and weeds density matrix computation with overlapping control).The proposed strategy has been conveniently tested and also the different processes defining each module, verifying their performances in terms of accuracies and processing times. Comparative analyses have also been carried out against some existing processes, also with acceptable performances.

Acquisition of Agronomic Images with Sufficient Quality by Automatic Exposure Time Control and Histogram Matching

Agronomic images in Precision Agriculture are most times used for crop lines detection and weeds identification; both are a key issue because specific treatments or guidance require high accuracy. Agricultural images are captured in outdoor scenarios, always under uncontrolled illumination. CCD-based cameras, acquiring these images, need a specific control to acquire images of sufficient quality for greenness identification from which the crop lines and weeds are to be extracted. This paper proposes a procedure to achieve images with sufficient quality by controlling the exposure time based on image histogram analysis, completed with histogram matching. The performance of the proposed procedure is verified against testing images.