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

A Handheld low-mass, impact instrument to measure nondestructive firmness of fruit

A portable, handheld impact firmness sensor was designed for nondestructive measurement of fruit firmness while the fruit remain attached to the tree or for use in other remote locations where the use of a benchtop instrument would be impractical. The instrument design was based on the low-mass, constant velocity, impact-type measurement concept. Validation tests of the handheld sensor using `Bartlett' pears from orchards in California and Washington showed excellent agreement (r2 = 0.92 and 0.96, respectively) with both ASAE Standard method S368.2 for determining the apparent modulus of intact fruit and the impact firmness scores from a commercial benchtop impact firmness instrument

Development of a precision 3-row synchronised transplanter

Commercial vegetable crop transplanters currently use several unsynchronised planting units mounted to a common transport frame. The objective of this work was to assess the performance of a new transplanting technology to improve the plant placement accuracy and spatiotemporal planting synchronization across adjacent rows, thus producing a grid-like planting pattern using adjacent vegetable crop transplanters. The feasibility of synchronisation of adjacent transplanting units for vegetable crops was demonstrated using tomato as the target crop. A colour, digital, high-speed computer vision analysis of the motion and dynamics of the plant trajectories of transplanted tomatoes was conducted. The high-speed video analysis led to the design and testing of an improved plant support mechanism to enhance the control and precision of the transplanting of vegetable crops. The absolute deviation values of the final location in the soil were reduced by approximately 25% for both the right planter and left planter compared to those in previous years. These results serve as the fundamental basis for a mechatronic system that can precisely transplant vegetable crops in a grid-like pattern across rows as a critical first step in a systematic approach to fully automated individual plant care

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

Evaluation of a Kiwifruit non-destructive firmness sensor

Non-deslructive firmness sensors have recently become available for packers and f r u í handlers although they derrand more ¡nforrratbn on their performance and reliability. A corrmerdal sensor based on low rrass irrpact has been tested on kiwifruit. Correlatton betweer the firrmess Índex given by the device and Magness-Taybr foro» was low (r3 = 0.594). Classiftoatbns modeled with dbcrirrinant analysls showed that it Is feasible to sort samples Into two firnness groups (96 to 91%), but dassiflcatbn into three dasses yields lower scores

Predicting pitting damage during processing in California clingstone peaches using color and firmness measurements

Nondestructive and destructive measures of color and firmness were studied to determine the feasibility of predicting the level of damage to clingstone peaches during mechanical pitting. Nondestructive and destructive measures of firmness were equally variable when measuring the firmness at three equatorial cheek locations (coefficient of variation of about 17%), both had inverse relationships with the level of pitting damage (r2 ranged from 0.70 to 0.83), and could classify peaches into two categories (those subject to and those not subject to pitting damage) with classification accuracies of 75.2% and 81.7%, respectively. Destructive firmness was not a good predictor of nondestructive firmness in clingstone peaches. Skin color was not a good predictor of flesh color in clingstone peaches, and flesh color was not a good predictor of potential for damage to clingstone peaches during mechanical pitting

Evaluación del funcionamiento de un sensor comercial de firmeza en pera. Estudio metrológico

Non-destructive firmness estimation of fruits has recently become available for packers, although they demand more information on their performance and reliability. A commercial device based on low mass impact has been tested in this work, and it has been applied on pears. Correlation between the firmness Índex gíven by the device and Magness-Taylor forcé was low (r=0.896). Classifications modeled with discrlmínant analysis showed that it is feasible sort samples into two firmness groups 96 to 91 % of correct classification for pear). Classification into three classes yields lower scores. A study searching for sources of variation ¡n the measurement showed that the distance sensor-fruit, the displacement from the center, and the operating pressure affect the reading ¡n a significant way

A Concept Paper on Networks of Excellence for Research and Education

Research and education ecosystems, foundational components of knowledge-based economies, are relatively underdeveloped in Latin America. The entire ecosystem of a research university — including resources, corporate partnerships, and research — must capitalize on a symbiosis between the research, education and commercialization missions. A university cannot transform unilaterally nor can universities sustain the required transformation without government and industry participation. Initiatives to accelerate the development of research university ecosystems are critical for the realization of knowledge-based economies and resilient civil societies. To accelerate the development of research and education ecosystems across the Americas, the authors propose to establish “Networks of Excellence” in key focus areas. Each Network of Excellence will be multi-institutional, multi-sector (university, corporate, government, NGO) and multi-national. These multi-faceted networks will allow participants to define and share programs, policies, and content, significantly leverage the resources provided for related programs, and identify opportunities to leapfrog existing programs. Proposed themes for networks include regional grand challenges and cross-cutting capabilities