Machine Vision Systems – A Tool for Automatic Color Analysis in Agriculture

It was in the early 1960s when machine vision systems initiated researchers and developers have worked on building machines that perform tasks of acquisition, processing, and analysis of images in a wide range of applications for different areas. Currently, along with the new technological advances in electronics, computer systems, image processing, pattern recognition, and mechatronics, it has arose the opportunity to improve machine vision systems development with affordable implementations at lower cost. A machine vision system is the combination of several high-tech techniques, including both hardware and software, used to acquire, process, and analyze images on a machine, which contributes with a set of tools for the extraction of features, such as color and dimension parameters, texture, chemical components, disease detection, freshness, assessment, modeling, and control, among others. Based on former paragraphs, we could say that machine vision systems are appropriate to improve the actual agricultural systems making them more useful, efficient, practical, and reliable

Diseño e implementación de un sistema de reconocimiento de naranjas para el robot GIO 1 usando visión asistida por computador

Investigación TecnológicaEn este documento trata de la implementación de un sistema de reconocimiento de naranjas para el robot GIO1 usando visión asistida por computador, que será desarrollado con el fin de mejorar las características de la plataforma robótica GIO 1, para que este sea capaz de diferenciar las naranjas de otros objetos (piedras u otros frutos) en entornos controlados.PregradoIngeniero Electrónic

Fruit Detection and Tree Segmentation for Yield Mapping in Orchards

Accurate information gathering and processing is critical for precision horticulture, as growers aim to optimise their farm management practices. An accurate inventory of the crop that details its spatial distribution along with health and maturity, can help farmers efficiently target processes such as chemical and fertiliser spraying, crop thinning, harvest management, labour planning and marketing. Growers have traditionally obtained this information by using manual sampling techniques, which tend to be labour intensive, spatially sparse, expensive, inaccurate and prone to subjective biases. Recent advances in sensing and automation for field robotics allow for key measurements to be made for individual plants throughout an orchard in a timely and accurate manner. Farmer operated machines or unmanned robotic platforms can be equipped with a range of sensors to capture a detailed representation over large areas. Robust and accurate data processing techniques are therefore required to extract high level information needed by the grower to support precision farming. This thesis focuses on yield mapping in orchards using image and light detection and ranging (LiDAR) data captured using an unmanned ground vehicle (UGV). The contribution is the framework and algorithmic components for orchard mapping and yield estimation that is applicable to different fruit types and orchard configurations. The framework includes detection of fruits in individual images and tracking them over subsequent frames. The fruit counts are then associated to individual trees, which are segmented from image and LiDAR data, resulting in a structured spatial representation of yield. The first contribution of this thesis is the development of a generic and robust fruit detection algorithm. Images captured in the outdoor environment are susceptible to highly variable external factors that lead to significant appearance variations. Specifically in orchards, variability is caused by changes in illumination, target pose, tree types, etc. The proposed techniques address these issues by using state-of-the-art feature learning approaches for image classification, while investigating the utility of orchard domain knowledge for fruit detection. Detection is performed using both pixel-wise classification of images followed instance segmentation, and bounding-box regression approaches. The experimental results illustrate the versatility of complex deep learning approaches over a multitude of fruit types. The second contribution of this thesis is a tree segmentation approach to detect the individual trees that serve as a standard unit for structured orchard information systems. The work focuses on trellised trees, which present unique challenges for segmentation algorithms due to their intertwined nature. LiDAR data are used to segment the trellis face, and to generate proposals for individual trees trunks. Additional trunk proposals are provided using pixel-wise classification of the image data. The multi-modal observations are fine-tuned by modelling trunk locations using a hidden semi-Markov model (HSMM), within which prior knowledge of tree spacing is incorporated. The final component of this thesis addresses the visual occlusion of fruit within geometrically complex canopies by using a multi-view detection and tracking approach. Single image fruit detections are tracked over a sequence of images, and associated to individual trees or farm rows, with the spatial distribution of the fruit counting forming a yield map over the farm. The results show the advantage of using multi-view imagery (instead of single view analysis) for fruit counting and yield mapping. This thesis includes extensive experimentation in almond, apple and mango orchards, with data captured by a UGV spanning a total of 5 hectares of farm area, over 30 km of vehicle traversal and more than 7,000 trees. The validation of the different processes is performed using manual annotations, which includes fruit and tree locations in image and LiDAR data respectively. Additional evaluation of yield mapping is performed by comparison against fruit counts on trees at the farm and counts made by the growers post-harvest. The framework developed in this thesis is demonstrated to be accurate compared to ground truth at all scales of the pipeline, including fruit detection and tree mapping, leading to accurate yield estimation, per tree and per row, for the different crops. Through the multitude of field experiments conducted over multiple seasons and years, the thesis presents key practical insights necessary for commercial development of an information gathering system in orchards

Automatic plant features recognition using stereo vision for crop monitoring

Machine vision and robotic technologies have potential to accurately monitor plant parameters which reflect plant stress and water requirements, for use in farm management decisions. However, autonomous identification of individual plant leaves on a growing plant under natural conditions is a challenging task for vision-guided agricultural robots, due to the complexity of data relating to various stage of growth and ambient environmental conditions. There are numerous machine vision studies that are concerned with describing the shape of leaves that are individually-presented to a camera. The purpose of these studies is to identify plant species, or for the autonomous detection of multiple leaves from small seedlings under greenhouse conditions. Machine vision-based detection of individual leaves and challenges presented by overlapping leaves on a developed plant canopy using depth perception properties under natural outdoor conditions is yet to be reported. Stereo vision has recently emerged for use in a variety of agricultural applications and is expected to provide an accurate method for plant segmentation and identification which can benefit from depth properties and robustness. This thesis presents a plant leaf extraction algorithm using a stereo vision sensor. This algorithm is used on multiple leaf segmentation and overlapping leaves separation using a combination of image features, specifically colour, shape and depth. The separation between the connected and the overlapping leaves relies on the measurement of the discontinuity in depth gradient for the disparity maps. Two techniques have been developed to implement this task based on global and local measurement. A geometrical plane from each segmented leaf can be extracted and used to parameterise a 3D model of the plant image and to measure the inclination angle of each individual leaf. The stem and branch segmentation and counting method was developed based on the vesselness measure and Hough transform technique. Furthermore, a method for reconstructing the segmented parts of hibiscus plants is presented and a 2.5D model is generated for the plant. Experimental tests were conducted with two different selected plants: cotton of different sizes, and hibiscus, in an outdoor environment under varying light conditions. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images. The results show an observed enhancement in leaf detection when utilising depth features, where many leaves in various positions and shapes (single, touching and overlapping) were detected successfully. Depth properties were more effective in separating between occluded and overlapping leaves with a high separation rate of 84% and these can be detected automatically without adding any artificial tags on the leaf boundaries. The results exhibit an acceptable segmentation rate of 78% for individual plant leaves thereby differentiating the leaves from their complex backgrounds and from each other. The results present almost identical performance for both species under various lighting and environmental conditions. For the stem and branch detection algorithm, experimental tests were conducted on 64 colour images of both species under different environmental conditions. The results show higher stem and branch segmentation rates for hibiscus indoor images (82%) compared to hibiscus outdoor images (49.5%) and cotton images (21%). The segmentation and counting of plant features could provide accurate estimation about plant growth parameters which can be beneficial for many agricultural tasks and applications

Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks

Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union (IOU) are 89.53, 95.33, and 82.00%, respectively. The detection and point cloud segmentation for each grape took approximately 1.7 s. The demonstrated performance of the developed method indicates that it can be applied to grape-harvesting robots

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Plant Diseases

Plant pathogens, the causal agent of infectious plant diseases, influence our lives more than just as an economic impact through yield lost. The study of plant pathogens has given rise to the development of new sciences, new technologies for plant breeding, and the agrochemical industry for pesticide developments. Yet, all our actions and efforts to suppress or eradicate them constantly pressures these various organisms to evolve and adapt for survival. Therefore today, when facing climate changes, accelerated transport of plants and plant products, and world population growth, we have to ask quo vadis phytopathology. Like Alice in Wonderland “If we wish to go anywhere we must run twice as fast as that” so we need to constantly broaden our knowledge. However, today’s literature abounds with knowledge about plant pathogens. Hence, this book intends to present to the reader all the latest material and knowledge about plant pathogens, changes or refinements in plant disease epidemiology, and new approaches and materials used for plant pathogen control. Hopefully, this book will be of interest to those working within the field and looking for an up-to-date introduction. We hope it also interests students and thereby, will influence the future development of phytopathology and our better coexistence with plant pathogens

Guideline on therapeutic dentistry for the 5-th term

РУКОВОДСТВАСТОМАТОЛОГИЯ ЛЕЧЕБНО-ВОССТАНОВИТЕЛЬНАЯСТОМАТОЛОГИЯ ТЕРАПЕВТИЧЕСКАЯИНОСТРАННЫЕ СТУДЕНТЫУЧЕБНО-МЕТОДИЧЕСКИЕ ПОСОБИЯПособие составлено в соответствии с учебной программой для медицинских вузов по терапевтической стоматологии. Предназначено для внутреннего использования