Обработка данных аэрофотосъемки в задачах мониторинга сельскохозяйственных полей

В данной статье описаны результаты исследования применимости некоторых подходов для решения задачи определения заболевания сельскохозяйственных полей по данным аэрофотосъемки. Использо- вались следующие подходы: пороговая сегментация по оттенку и насыщенности, нечеткая сегментация, вычисление фрактальных сигнатур (последний подход использован к каждому из цветовых каналов снимков отдельно). Также использована нечеткая сегментация по шести каналам: три цветовых канала и три канала, представляющие собой результат вычисления фрактальных сигнатур по цветовым каналам.In the paper the results of research of applicability of some approaches for the decision of a problem of agricultural fields disease definition are described on evidence derived from airphotography by the example of potato fields. For definition of disease the following approaches were used: threshold segmentation on a hue and saturations, fuzzy segmentation, fractal signatures calculation. Also indistinct segmentation on six channels is used: three color channels and three channels representing result of fractal signatures calculation on color channels

Detecção e localização de faces em imagens

Mestrado em Engenharia Electrónica e TelecomunicaçõesDesde a existência do Homem que este utiliza a visão para detectar e localizar faces humanas para identificar e interagir com a face identificada de forma apropriada. Em visão computacional, o simples processo efectuado pelo Homem de detectar e localizar faces em imagens é bastante complexo. Nos últimos anos houve um grande aumento de pesquisa sobre a detecção e localização de faces em imagens, devido ao facto de ser o primeiro passo em qualquer sistema de reconhecimento facial. As inúmeras dificuldades que as características da face apresentam (tamanho, posição, orientação, entre outras) são um desafio para a implementação do algoritmo. Existe um grande número de técnicas para resolver esses problemas. Neste trabalho foi implementado um algoritmo para detecção e localização de faces em imagens que num primeiro passo consiste na detecção de pele, através de 3 métodos com 3 espaços de cores distintos. Posteriormente é aplicado o método do template a algumas regras geométricas baseadas no conhecimento para detectar se as diferentes zonas de pele detectadas são face ou não-face. Por ultimo é efectuada a localização da face na imagem

Desarrollo de índices multiespectrales para el monitoreo de la fertilización orgánica de plantas de tomate en estadios iniciales

[EN] A crop monitoring system was developed for monitor organic fertilization status of tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3+5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550nm), red (660nm), red edge (735nm), near infrared (790nm) + 16MP RGB and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 ml, T1: 6.25 ml, T2: 12.5 ml and T3: 25 ml) and the vermicompost was added in the 1st (T0: 0ml; T1: 75 ml; T2:150ml; T3: 300 ml) and in the 5th week (T0: 0ml; T1: 237,5 ml; T2:475ml; T3: 950 ml). The trial was conducted in a greenhouse and 192 images were taken with each lens. An plant segmentation algorithm was developed and several vegetation indexes were calculated: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red-Edge Normalized Difference Vegetation Index (RENDVI), Nonlinear Vegetation Index (NLI), Optimized Soil Adjusted Vegetation Index (OSAVI), Green Ratio Vegetation Index (GRVI), Simple Ratio (SR), Modified Simple Ratio (MSR), Structure Intensive Pigment Index 2 (SPI2) and Leaf Chlorophyll Index (LCI). Multiple morphological features were obtained through image processing and the results were compared between treatments using Tukey¿s HSD test with 1% of probability. The morphological features such as, Area, Filled Area, Convex Area, Perimeter, Major Axis Length, Minor Axis Length and Equivalent Diameter revealed to be feasible to distinguish between the control and the organic fertilized plants despite the vegetation indexes. The system was developed in order to be assembled in a precision organic fertilization robotic platform.[ES] Se ha desarrollado un sistema de monitoreo de cultivos para supervisar el estado nutricional de las plantas de tomate en las primeras etapas de desarrollo. Se utilizó un enfoque automático y no destructivo para analizar las plantas de tomate con diferentes niveles de fertilizante orgánico soluble en agua (3 + 5 NK) y humus de lombriz. El sistema de evaluación estaba compuesto por una cámara multiespectral con cinco lentes: verde (550nm), rojo (660nm), borde rojo (735nm), infrarrojo cercano (790nm) + 16MP RGB y un sistema de procesamiento de imágenes computacional. El fertilizante soluble se aplicó semanalmente en cuatro tratamientos diferentes: (T0: 0 ml, T1: 6.25 ml, T2: 12.5 ml y T3: 25 ml) y el vermicompost se añadió en la primera (T0: 0 ml; ml, T2: 150 ml, T3: 300 ml) y en la quinta semana (T0: 0 ml, T1: 237.5 ml, T2: 475 ml, T3: 950 ml). El ensayo se realizó en invernadero y se tomaron 192 imágenes con cada lente. Se desarrolló un algoritmo de segmentación y múltiples índices de vegetación fueron calculados: Índice de Vegetación de Diferencia Normalizada (NDVI), Índice de Vegetación de Diferencia Normalizada Verde (GNDVI), Índice de Vegetación de Diferencia Normalizada de Borde Rojo (RENDVI), Índice de Vegetación No Lineal (NLI), Índice de Vegetación Ajustado y Optimizado para el Suelo (OSAVI), Índice de Vegetación de Proporción Verde (GRVI), Proporción Simple (SR), Proporción Simple Modificada (MSR), Índice de Pigmento Intensivo de Estructura 2 (SPI2) e Índice de Clorofila de la Hoja (LCI). Adicionalmente al cálculo de índices, se obtuvieron múltiples características morfológicas a través del procesamiento de imágenes y los resultados se compararon entre los tratamientos utilizando la prueba HSD de Tukey con un 1% de probabilidad. Las características morfológicas tales como: Área, Área rellena, Área convexa, Perímetro, Longitud del eje mayor, Longitud del eje menor y Diámetro Equivalente se revelaron más útiles para distinguir entre el control y las plantas fertilizadas orgánicamente que los índices de vegetación. El sistema fue desarrollado para ser ensamblado en una plataforma robótica de fertilización orgánica de precisión.Cardim Ferreira Lima, M. (2019). Development of Multispectral Indices for Organic Fertilization Monitoring in Tomato Plants at Early Stages. Universitat Politècnica de València. http://hdl.handle.net/10251/129471TFG

A Review Of Vision Based Defect Detection Using Image Processing Techniques For Beverage Manufacturing Industry

Vision based quality inspection emerged as a prime candidate in beverage manufacturing industry. It functions to control the product quality for the large scale industries; not only to save time, cost and labour, but also to secure a competitive advantage. It is a requirement of International Organization for Standardization (ISO) 9001, to appease the customer satisfaction in term of frequent improvement of the quality of products and services. It is totally impractical to rely on human inspector to handle a large scale quality control production because human has major drawback in their performance such as inconsistency and time consuming. This article reviews defect detection using image processing techniques for beverage manufacturing industry. There are comparative studies on techniques suggested by previous researchers. This review focuses on shape defect detection, color concentration inspection and level of liquid products measurement in a container. Shape, color and level defects are the main concern for bottle inspection in beverage manufacturing industry. The development of practical testing and the services performance are also discussed in this paper

An adaptive image processing algorithm for field plant population analysis based on UAV imaging system

Plant population is among the most important items of agricultural data. In crop science, plant population determines canopy closure time, plant spacing, and weeds competition. For farmers, if plant population can be obtained in time, it can help in crop management decisions on replanting, fertilizer application, and estimation of yield. For agricultural manufacturing companies, they need plant population data to check the accuracy between planter setting and actual plant population. However, counting of plant population is difficult in agriculture. Manual counting is the main counting method to get plant population. For 30 inch rows, researchers or farmers measure 17 feet 5 inches and count the number of plants along the length to get plant population of 1/1,000 acre. Then the count is multiplied by 1,000 to get the per acre population. This method is a sampling estimation with lots of time cost. To improve the counting efficiency, image-processing methods have been used to identify individual plants and count plant numbers in small experimental plots with a ground camera. But those methods are limited to an experimental environment with fixed cameras, which is hard to apply in large fields. With advanced technologies, it is possible to collect field images by using an unmanned aerial vehicle (UAV). In the most recent research, low-altitude (under 10 m) drone images have been used to estimate plant density of wheat crops. New algorithms of plant counting have been proposed for low-altitude drone images. But the limitation is that low-altitude flights only cover small field area and may damage plants near the flight path. In this study, a novel image-processing algorithm is developed for measuring plant population from medium-altitude (25 m - 50 m) drone images. Those images are collected by UAV image system for an 80 acres field. Based on a large number of field images, the algorithm was developed with overall consideration of crop color, crop space, growth status, and plant row information. Then, drone images are processed by the algorithm to generate a plant population map of entire field. Finally, the population map was checked by real field counting results. The population results are generated from medium-altitude drone images. When compared real field check points with manually counted populations, the difference of their mean population is less than1,000 plants/acre. The R2 between the manually checked points and the population map is 0.82, which means the two datasets in these sample points are highly correlated. Then for the two groups of data, statistical analysis by paired-samples t test yielded a p value of 0.062. There is no significant difference between two groups’ data. For this proposed method, the UAV imaging system can cover an 80-acre field in 10 minutes, and the plant population map of the entire field can be generated by the algorithm within 120 minutes, which would cost hundreds of hours for manual population counting. For the experiment field, planter population setting was 31,000/acre, while the actual counted plant population is averaged at 27,000/acre, 12.9% less than setting

Automatic classification of grassland herbs in close-range sensed digital colour images

The broad-leaved dock (Rumex obtusifolius L. (RUMOB)) is one of the most harmful and persistent weed species on European grassland and it has been spread into the temperate grassland regions throughout the world. Large dry matter contributions of Rumex obtusifolius L. reduce the quality of the standing forage considerably because of the poor palatability of leaves and tillers and withdraw water and nutrient from surrounding plants. For Central Europe it is estimated that more than 80% of all herbicides used in conventional grassland farming are used to control Rumex species. Until today, herbicides are applied over the whole field, even if Rumex plants are not homogeneously distributed area-wide. Recently developed precision farming techniques based on weed mapping that use mainly image processing, enable site-specific spraying of weeds in arable crops. Until today those techniques have not been applied to grassland weed sensing. Compared to the identification of isolated individual plants on a rather uniform soil background in arable crops, image processing for a more complex environment as grassland requires a different approach. The aim of the thesis was to develop an image processing procedure for automatic detection of grassland weeds using close-range digital colour images, focussing on the detection of RUMOB. A field experiment has been established with grassland plots populated with RUMOB and the other typical broad leaved grassland weeds Taraxacum officinale Web. (TAROF) and Plantago major L. (PLAMA). Digital colour images have been taken from around 1.5 m above ground at three dates in 2005. Image acquisition was done automatically by a vehicle driven on rails alongside to the experimental plots, whereby nearly constant recording geometry conditions were guaranteed. Images were taken during cloud cover in order to avoid direct sunlight. Using the images from 2005 an object-oriented image classification has been developed. Thereby, the leaves of the weeds were separated from the background using parameters of homogeneity and morphology, resulting in a binary image. The remaining image objects in the binary image were contiguous regions of neighbouring pixels related to the object classes of the weed species, soil, and residue objects. Geometrical-, colour and texture features were calculated for each of these objects. Discriminant analysis exhibited that colour and texture features contribute most to the discriminating of objects into the different classes. In a Maximum Likelihood classification these features were used to differentiate the objects into their respective classes. High overall accuracies and even higher RUMOB detection rates were achieved. The algorithm has been modified and applied to images of varying image resolutions. High classification accuracies have been achieved with all image resolutions, whereby the processing time could be improved for images with lowest resolutions. Images were taken at 13 dates over the two grassland growths in 2006. In all the images the plant species were classified automatically using the developed image classification integrated in a graphical user interface software. The coordinates of the objects classified as RUMOB were transformed into Gauss-Krueger system to generate distribution maps of this weed. The combination of object density and area further decreased its misclassifications. RUMOB classification rates across the season were analysed and phenological stages have been identified on which classification performed best. The results demonstrate high potential of machine vision for weed detection in grassland. A classification procedure based on image analysis and Geographic Information System (GIS) post-processing has been developed for detecting Rumex obtusifolius L. and other weeds in grassland with high accuracy. Future projects might focus on the application to real grassland conditions and the derivation of RUMOB distribution maps. Thus, herbicide application maps can be calculated, utilized for site-specific weed control. The development of an image acquisition unit to be mounted on a driving vehicle along with a standardization of image recording is going to be the main focus

Image-based mapping system for transplanted seedlings

Master of ScienceDepartment of Mechanical and Nuclear EngineeringDale SchinstockDevelopments in farm related technology have increased the importance of mapping individual plants in the field. An automated mapping system allows the size of these fields to scale up without being hindered by time-intensive, manual surveying. This research focuses on the development of a mapping system which uses geo-located images of the field to automatically locate plants and determine their coordinates. Additionally, this mapping process is capable of differentiating between groupings of plants by using Quick Response (QR) codes. This research applies to green plants that have been grown into seedlings before being planted, known as transplants, and for fields that are planted in nominally straight rows. The development of this mapping system is presented in two stages. First is the design of a robotic platform equipped with a Real Time Kinematic (RTK) receiver that is capable of traversing the field to capture images. Second is the post-processing pipeline which converts the images into a field map. This mapping system was applied to a field at the Land Institute containing approximately 25,000 transplants. The results show the mapped plant locations are accurate to within a few inches, and the use of QR codes is effective for identifying plant groups. These results demonstrate this system is successful in mapping large fields. However, the high overall complexity makes the system restrictive for smaller fields where a simpler solution may be preferable

Development of remote sensing techniques for the implementation of site-specific herbicide management

xii, 106 leaves : ill. (col. ill.) ; 29 cmSelective application of herbicide in agricultural cropping systems provides both economic and environmental benefits. Implementation of this technology requires knowledge of the location and density of weed species within a crop. In this study, two image classification techniques (Artificial Neural Networks (ANNs) and Maximum Likelihood Classification (MLC)) are compared for accuracy in weed/crop species discrimination. In the summer of 2005, high spatial resolution (1.25mm) ground-based hyperspectral image data were acquired over field plots of three crop species seeded with two weed species. Image data were segmented using a threshold technique to identify vegetation for classification. The ANNs consistently outperformed MLC in single-date and multitemporal classification accuracy. With advancements in imaging technology and computer processing speed, these network models would constitute an option for real-time detection and mapping of weeds for the implementation of site-specific herbicide management

Automated detection and control of volunteer potato plants

High amounts of manual labor are needed to control volunteer potato plants in arable fields. Due to the high costs, this leads to incomplete control of these weed plants, and they spread diseases like Phytophthora infestans to other fields. This results in higher environmental loads by curative spraying of crop protection chemicals, which is in contradiction to the required decreased use of crop protection chemicals to save the environment. Therefore, the main objective of this thesis was “to develop a system for automated detection and control of volunteer potato plants”. A systematic design approach was used to define a program of requirements and to identify and order possible solutions to accomplish the detection and control. The main requirements were a travel speed of up to 2 m s-1, resolution of control at least 10×10 mm, work under variable natural light conditions, control of volunteer plants > 95%, and undesired control of sugar beet plants - Detection of volunteer potato plants, - Control of volunteer potato plants, - Real-time implementation of integrated detection and control on a proof of principle machine. For the purpose of detection of volunteer potato plants, the narrow band spectral reflectance properties of volunteer potato plants and sugar beet plants were analyzed. Narrow band spectral measurements were done in 2006 and 2007 on two different fields. This resulted in 15 datasets on clay and sand soil. Discriminating wavebands were selected and classified with neural networks and statistical discriminant analysis. A neural network with two hidden neurons performed best for classification. Two sensors were used covering the range from 450 to 900 nm and from 900 to 1650 nm. Both visible and near infra-red wavebands were responsible for discrimination. From the analysis 450, 765, and 855 nm from sensor 1 and 900, 1440, and 1530 nm from sensor 2 were identified as important discriminative wavebands. However, the discriminative wavelengths depended on field and crop status and could not be generalized. Ten wavebands that were optimally adapted to the datasets gave 99% true negative classification of volunteer potato plants. On the other hand, a fixed set of three wavebands that was not adapted to the individual datasets gave 80% true negative classification of volunteer potato plants. This indicates that adaptive feature sets are required for classification. The development of the machine vision detection system started with measurements in 2005. Color based detection showed that the difference in classification results was larger between fields than the difference between a static neural network and static Bayesian classification. Then, machine vision measurements in 2006 with a color camera under changing and constant natural light conditions showed that crop and weed properties change within a field. An adaptive instead of static classification increased classification accuracy from 34.9% to 67.7% under changing light conditions. Under constant natural light conditions, the classification accuracy increased from 84.6% to 89.8%. So, adaptive classifiers are required and were implemented in the further research as these gave significantly higher classification results. As a next step, besides a color camera also a near-infrared camera was used for imaging within the proof of principle machine, as this gave a better feature set for classification. Additionally, the field of view of the cameras was shielded and artificial light was used to maintain constant light conditions. For the real-time implementation, an unsupervised adaptive Bayesian classifier was used. The crop row position and crop row width were determined and a Kalman filter improved tracking of the rows, to adapt to the varying properties of the crop in the field. Data from between the crop rows was trained as the volunteer potato class and data from within the crop row was trained as the sugar beet class. This resulted in good quality training data for the Bayes classifier. The system was unsupervised, as it learned and trained itself based on row recognition. The features that were used for training and classification were: blue, hue, saturation, excessive green, red minus blue, near-infrared and near-infrared difference vegetation index (NDVI). These feature values within the training data were continuously locally adapted, in two first-in-first-out buffers both with an area of 500 cm2 for sugar beet and volunteer potato plants. Measurements were done on seven days in 2007 and 2008. The results showed a trade-off between the percentage of correct classified volunteer potato plants and the percentage of misclassification of sugar beet plants. In one of the fields 96.6% volunteer potato classification and 8.0% sugar beet misclassification was achieved. Connected to the detection system was a micro-sprayer that applied glyphosate in gel to the volunteer potato plants. Spraying gel through a micro-sprayer was innovative. This proved to work in the application of glyphosate on plants. As knowledge of the dose response of glyphosate on potato was outdated and could not be used for plant specific application, a dose-response study was done with flat fan nozzles on 120 potato plants to determine the efficacy of glyphosate. The effect parameters tuber weight and photosynthesis activity were analyzed with log-logistic nonlinear regression methods. This resulted in an amount of 843 μg a.e. per plant for reduction of tuber weight and photosynthesis with 90%. This amount was applied on plants with a height of 6.1±1.39 cm and an area of 53.3±19.6 cm2. As glyphosate was to be applied with a micro-sprayer, the dose-response study was extended to 500 greenhouse grown potato plants. Five application methods were used: 1) flat fan water application, 2) flat fan gel application, 3) micro-sprayer low density distribution, 4) micro-sprayer medium density distribution, and 5) micro-sprayer high density distribution. As effect parameters again tuber weight, photosynthesis activity, and in addition shoot dry weight were used. They were analyzed with ANOVAs and box-plots. The micro-sprayer dense distribution with 3022 droplets m-2 and 3.3 mg per droplet had the best efficacy. The micro-sprayer controlled the volunteer potato plants with less glyphosate compared to flat fan nozzles. Furthermore, it had a centimeter precision resolution and low risks of unwanted crop damage. With real-time hardware, machine vision detection and micro-sprayer were integrated to a proof of principle machine. A travel speed of 0.8 m s-1 was reached with the proof of principle machine and it had an approximated capacity of 2.5 hrs ha-1. This was the maximum that could be realized as the micro-sprayer valve actuation frequency was maximally 80 Hz. The image processing time for one image of 0.2 m length was 195 ms. At this travel speed automated feedback systems on the operation of the system are required to support and replace human surveillance. Therefore, the Fréchet distance measure between multivariate distributions was introduced as quality indicator of classification performance. The Fréchet distance measure was significantly smaller when the classification performance was low, as identified on ground truth determined classification results afterwards. This proves that the performance could be predicted with a distance measure between multivariate distributions. In case of poor predicted classification performance, the application of glyphosate with the micro-sprayer can be halted to prevent unwanted crop damage and economic losses. The accuracy of application was ±1.4 cm in longitudinal direction and ±0.75 cm in transversal direction. During a field trial, up to 84% of the volunteer plants were controlled with 1.4% unwanted controlled sugar beet plants. To sum up, within this research a proof of principle machine for automated detection and control of volunteer potato plants in sugar beet fields has successfully been developed. The system performed closely to the requirements that were set in the start-up of the project. The percentage of 95% controlled volunteer potato plants can be reached. On the other hand, the travel speed still has to be increased from 0.8 m s-1 to 2.0 m s-1. The system is an example of new technology that can be developed for practical applications to reduce the amount of required labor and to reduce the crop protection inputs for weed control in arable farming. <br/