Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

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

Automatic weed control system for processing tomatoes

WORLD CONGRESS OF THE INTERNATIONAL COMMISSION OF AGRICULTURAL AND BIOSYSTEMS ENGINEERING (17) (17.2010.QUEBEC CITY, CANADA)This study describes a fully automatic system developed at UC Davis for intra-row mechanical weed control for processing tomatoes in California. We developed a novel weed control system using a real-time kinematics (RTK) global positioning system (GPS) to automatically control the path of a pair of weed knives based upon an automatically generated GPS plant map. The system was capable of precisely guiding mechanical weed knives within the seedline of the crop row and around the crop plants as the system was pulled along the row. In this study, processing tomato plants were transplanted using a GPS-enabled transplanter, which developed a precision plant map documenting the geo-spatial location of each tomato plant. At the time of first cultivation, a few weeks after planting, the GPS-controlled weed knives were operated in seven tomato rows. The weed knives were set to "open" 6 cm prior to reaching, and "close" 6 cm after passing each tomato plant, killing weeds between tomato plants when the knives were in the closed position. Results show that the average distance between knife opening and closing events was 12.4 cm with a standard deviation of 1.4 cm. The standard deviation of the opening and closing positions (relative to the crop plant) was 2.08 and 2.11 cm, respectively. These results demonstrate the feasibility of using RTK-GPS to automatically control a mechanical weed control system for sustainable production of row crops

Development of an RTK GPS plant mapping system for transplanted vegetable crops.

This study investigated the feasibility of using real-time kinematics (RTK) GPS to automatically map the locations of tomato transplants in the field as they are planted using a vegetable crop transplanter retrofitted with an RTK GPS receiver, and an on board real-time controller. Two detection methods were evaluated for sensing plant location during planting. One method used an infrared light beam sensor to detect the stem location of each plant immediately after planting. The second method used an absolute shaft encoder mounted on the planting wheel to sense the location that each plant was placed in the soil. Odometry was used to determine the actual Easting and Northing GPS coordinates of each plant by interpolation from the original RTK GPS data stream. A field test was conducted to compare the accuracy of this transplant map with actual plant location. The average absolute differences between the automatically generated transplant map and the plant location determined by GPS survey was 0.8 to 2.1 cm in the Northing direction and 1.6 to 3.8 cm in the Easting direction, which was also the travel direction. Results suggest the feasibility of creating an accurate plant map using an RTK GPS equipped transplanter

Optical sensor to determine plant spacing for precise application

Congreso Agroingeniería 2015. Orihuela (Alicante). 1 a 3 de junio de 2015La automatización en el manejo de la mala hierba de forma individualizada está siendo una realidad cada vez más cercana, tanto objetivos de eficiencia como de beneficio económico se están consiguiendo. En un futuro muy próximo la mecanización y su diseño tendrá que contemplar técnicas de monitorización o detección de precisión. El objetivo de este estudio fue evaluar la capacidad de un sensor óptico para determinar el espacio entre las plantas de una misma línea de cultivo en diferentes condiciones: laboratorio y campo. Este equipo de trabajo ha investigado la detección del cultivo con un sensor fotoeléctrico de infrarrojo (880 nm) en modo opuesto. La barrera fotoeléctrica de transmisión, usa un par de sensores y al paso de la planta por la cortina de luz interrumpe el haz. La señal de luz se recibe de forma simultánea en tiempo real mediante un sistema de control de alta velocidad. El sensor fotoeléctrico fue capaz de detectar la planta de tomate y por tanto de terminar la separación entre ellas de forma muy precisa. El uso de sistemas de detección como este puede dar lugar a una nueva era que permita el control en la línea de cultivo de la mala hierba de forma más económica y a la automatización de la operación. El control preciso de la mala hierba es un reto importante para este grupo, por ello tenemos previsto seguir trabajando en esta línea.Automation of individual crop plant care in commercial vegetable crop fields has increased practical feasibility and improved efficiency and economic benefit. Its systems approach is taken in the mechanization engineering design by the incorporation of precision sensing techniques. The objective of this study was design-sensing capabilities in an implement for measuring plant spacing under different test conditions: laboratory and field. For that proposal, a photoelectric transmission barrier, by using an optical light curtain transmitter and receiver, evaluated the interruption by the tomato stem of the light curtain, being recorded simultaneously in real-time by a high-speed embedded control system. The optical sensor provided tomato plant detection and therefore the spacing between tomato plants was determined with accuracy. The use of this detection system may result in a new era that allow for an online control of aggressive weeds and the automation of weeding tools, which we plan to pursue through future research.Junta de Andalucía P12-AGR-122

Helical Face Gear Development Under the Enhanced Rotorcraft Drive System Program

U.S. Army goals for the Enhanced Rotorcraft Drive System Program are to achieve a 40 percent increase in horsepower to weight ratio, a 15 dB reduction in drive system generated noise, 30 percent reduction in drive system operating, support, and acquisition cost, and 75 percent automatic detection of critical mechanical component failures. Boeing s technology transition goals are that the operational endurance level of the helical face gearing and related split-torque designs be validated to a TRL 6, and that analytical and manufacturing tools be validated. Helical face gear technology is being developed in this project to augment, and transition into, a Boeing AH-64 Block III split-torque face gear main transmission stage, to yield increased power density and reduced noise. To date, helical face gear grinding development on Northstar s new face gear grinding machine and pattern-development tests at the NASA Glenn/U.S. Army Research Laboratory have been completed and are described

Determination by optical systems of the inter-plant real distance for precise weed control

La automatización en el manejo de las malas hierbas de forma individualizada está siendo una realidad cada vez más cercana, atendiendo a objetivos tanto de eficiencia como de beneficios económicos. En un futuro muy próximo la mecanización y su diseño tendrá que contemplar técnicas de monitorización o detección de precisión. El objetivo de este estudio fue evaluar la capacidad de un sensor óptico para determinar la distancia entre las plantas de una misma línea de cultivo en condiciones de laboratorio y campo. En concreto, se ha investigado la detección de plantas con un sensor fotoeléctrico de infrarrojo (880 nm) en modo opuesto. El sensor fotoeléctrico fue capaz de detectar la planta de tomate y por tanto permitir conocer la separación entre ellas de forma muy precisa. El uso de sistemas de detección como este puede dar lugar a una nueva era que permita el control en la línea de cultivo de la mala hierba de forma más económica y a la automatización de la operación.Automation of individual crop plant care in comercial vegetable crop fields has increased practical feasibility and improved efficiency and economic benefit. Its systems approach is taken in the mechanization engineering design by the incorporation of precision sensing techniques. The objective of this study was design-sensing capabilities in an implement for measuring plant spacing under different test conditions: laboratory and field. The optical sensor provided tomato plant detection and therefore the spacing between tomato plants was determined with accuracy. The use of this detection system may result in a new era that allow for an online control of aggressive weeds and the automation of weeding tools, which we plan to pursue through future research

Developing New Tools to Determine Plant Spacing for Precise Agrochemical Application

CIGR - AgEng 2016 Aarhus, Denmark 26 - 29 JuneAdvances in the usage of computer imaging, communication technologies and the successful development of new techniques for precision agriculture have facilitated a smart-digital revolution in row crop agriculture in recent years. The use of a yield monitor, variable rate application (VRA) for fertilizer and herbicides, soil property maps and Global Navigation Satellite System (GNSS) technology has enabled the development of computer generated prescription maps for farm management. All these technologies are changing agricultural practices from simple mechanical operations to automated operations implemented by robotic-based systems. The automation of individual crop plant care in vegetable crop fields has increased its practical feasibility and improved efficiency and economic benefit. A systems-based approach is a key feature in the mechanization engineering design via the incorporation of precision sensing techniques. The objective of this study was to design sensing capabilities for implementation to measure plant spacing under different test conditions (California, USA and Andalucía, Spain). Three different optical sensors were used: an optical light curtain transmitter and receiver (880nm), a LiDAR sensor (905 nm), and an RGB camera. An active photoelectric transmission sensor, which contained 3 pairs of optical light curtain transmitters and receivers, evaluated the interruption by the tomato stem of the light curtain between the two devices, and was recorded simultaneously in real-time by a high-speed embedded control system. The LiDAR (model LMS 211 in California and LMS 111 in Spain, from SICK AG) was installed in a vertical orientation in the middle of the mobile platform. Additionally, a RGB spatial mosaicked image was used to adjust the data from the light beam and LiDAR sensor and obtain combined information (RGBD where D is for distance). These sensors were used to properly detect, localize, and discriminate between weed and tomato plants. The use of this detection system may result in a new technique that allows for the automatic control of aggressive weeds and the automation of weeding tools.Ministerio de Economía y Competitividad AGL2013-46343-RJunta de Andalucía P12-AGR-122

GPS-Based Intra-Row Weed Control System: Performance and Labor Savings

Researchers at UC Davis and elsewhere have demonstrated the technical feasibility of using RTK-GPS technology to automatically create crop plant maps at planting (Sun et al., 2010; Perez-Ruiz, 2011). This paper describes the development and field evaluation of a completely automatic system for intra-row weed control in tomato fields. This system uses an automatically generated GPS crop plant map to automatically control the path of a set of mechanical intra-row weed knives in order to kill weeds growing between tomato plants in the seedline. The manual labor savings were evaluated in controlled field trials and the labor savings benefits associated with the use of this system are reported