Luis Emmi

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.

New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.

Integrating Sensory/Actuation Systems in Agricultural Vehicles

In recent years, there have been major advances in the development of new and more powerful perception systems for agriculture, such as computer-vision and global positioning systems. Due to these advances, the automation of agricultural tasks has received an important stimulus, especially in the area of selective weed control where high precision is essential for the proper use of resources and the implementation of more efficient treatments. Such autonomous agricultural systems incorporate and integrate perception systems for acquiring information from the environment, decision-making systems for interpreting and analyzing such information, and actuation systems that are responsible for performing the agricultural operations. These systems consist of different sensors, actuators, and computers that work synchronously in a specific architecture for the intended purpose. The main contribution of this paper is the selection, arrangement, integration, and synchronization of these systems to form a whole autonomous vehicle for agricultural applications. This type of vehicle has attracted growing interest, not only for researchers but also for manufacturers and farmers. The experimental results demonstrate the success and performance of the integrated system in guidance and weed control tasks in a maize field, indicating its utility and efficiency. The whole system is sufficiently flexible for use in other agricultural tasks with little effort and is another important contribution in the field of autonomous agricultural vehicles.

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.

Fleets of robots for precision agriculture: a simulation environment

Purpose – The purpose of this paper is to propose going one step further in the simulation tools related to agriculture by integrating fleets of mobile robots for the execution of precision agriculture techniques. The proposed new simulation environment allows the user to define different mobiles robots and agricultural implements.Design/methodology/approach – With this computational tool, the crop field, the fleet of robots and the different sensors and actuators that are incorporated into each robot can be configured by means of two interfaces: a configuration interface and a graphical interface, which interact with each other.Findings – The system presented in this article unifies two very different areas – robotics and agriculture – to study and evaluate the implementation of precision agriculture techniques in a 3D virtual world. The simulation environment allows the users to represent realistic characteristics from a defined location and to model different variabilities that may affect the task perf...

Detailed Study of Amplitude Nonlinearity in Piezoresistive Force Sensors

This article upgrades the RC linear model presented for piezoresistive force sensors. Amplitude nonlinearity is found in sensor conductance, and a characteristic equation is formulated for modeling its response under DC-driving voltages below 1 V. The feasibility of such equation is tested on four FlexiForce model A201-100 piezoresistive sensors by varying the sourcing voltage and the applied forces. Since the characteristic equation proves to be valid, a method is presented for obtaining a specific sensitivity in sensor response by calculating the appropriate sourcing voltage and feedback resistor in the driving circuit; this provides plug-and-play capabilities to the device and reduces the start-up time of new applications where piezoresistive devices are to be used. Finally, a method for bypassing the amplitude nonlinearity is presented with the aim of reading sensor capacitance.

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.

Reducing fuel consumption in weed and pest control using robotic tractors

Method to reduce fuel consumption of robotic tractors on weed and pest control.This method reduces the emission of atmospheric pollutants.A system consumption model is implemented and validated.The system energy behavior is predicted using a 3D representation of the field.The results are demonstrated experimentally using real agricultural machines. A significant problem exists concerning contamination of the environment, especially air pollution, and the consequent climatic change. Considering that agricultural vehicles that use fossil fuels emit significant amounts of atmospheric pollutants, the main objective of this paper is to include techniques to reduce the fuel consumption in the controls system of robotic tractors used in agriculture tasks and thereby reduce the atmospheric emissions from these automated applications. To achieve this goal, the first step is to analyze fuel consumption in real time for each of the applications to be improved and to implement the consumption model of a robotic tractor for each task, considering the mechanical energy variations, the performance losses, the energy used to overcome friction and the energy required by the given task. For calculating the mechanical energy, the model considers the potential energy of the system, which is a function of the mass, elevation and gravity. The terrain elevations are estimated from GeoTIFF images of DEM data, which have a pixel size equal to 1arc second (approximately 30m at the Equator), and an accuracy of integer meters. Regarding the system mass, the possible loss of mass from applying the treatment is considered. For estimating the frictional forces, the rolling resistance coefficient of the terrain surface conditions is used.The consumption model has been validated experimentally using real agricultural vehicles and implements within the RHEA project (FP7-NMP 245986), in which the instantaneous fuel consumption was measured.This fuel reduction method is applied to three different treatments: weed control on herbaceous crops through the spraying of herbicides, weed control on fire-resistant crops with wide furrows through plowing and flame treatment, and pest control on trees through fumigation using insecticides.Finally, a fuel reduction procedure is applied to each task using the system model implemented to predict the energy requirements. This enables one to find the optimum path plan with respect to fuel consumption. These theoretical results are compared with the experimental results. In addition, the goal is to demonstrate the fuel reduction technique by performing field experiments to show that the use of this method of fuel reduction leads to an reduced fuel consumption and thus reduces atmospheric emissions from agricultural tasks. The results obtained revealed that this fuel reduction method significantly reduces the energy requirements, with the consequent reduction in fuel consumption and atmospheric pollutant emissions.

Improving the performance of piezoresistive force sensors by modeling sensor capacitance

Piezoresistive force sensors exhibit considerable lower accuracy compared with load cells and force measuring systems based in strain gauges, however a new method for measuring forces using piezoresistive sensors is described in this paper, leading to a considerable increase in the repeatability of force readings. The new method consists of reading sensors conductance and capacitance by applying DC and sinusoidal waveforms, thereby allow us to determine a multivariable estimation of force, instead of using the traditional, purely resistive model that has been used up to now. A study of sensor nonlinearities is also described which will allow us to determine the optimal setup to perform capacitance readings under sinusoidal excitation.