Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Statistics for Spatial Data.

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Hundreds of variants clustered in genomic loci and biological pathways affect human height

OUTPU T 29 OUTPU T 30 OUTPU T 31 OUTPU T 32 OUTPU T 25 OUTPU T 26 OUTPU T 27 OUTPU T 28 OUTPU T 21 OUTPU T 22 OUTPU T 23 OUTPU T 24 OUTPU T 17 OUTPU T 18 OUTPU T 19 OUTPU T 20 OUTPU T 13 OUTPU T 14 OUTPU T 15 OUTPU T 16 OUTPU T 9 OUTPU T 10 OUTPU T 11 OUTPU T 12 OUTPU T 5 OUTPU T 6 OUTPU T 7 OUTPU T 8 OUTPU T 1 OUTPU T 2 OUTPU T 3 OUTPU T 4 29 30 31 32 25 26 27 28 21 22 23 24 17 18 19 20 13 14 15 16 9

A User’s Guide

Original paper: A vision based row detection system for sugar beet

A texture–based weed classification method was developed. The method consisted of a low–level Gabor
wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This
classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass
categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur,
velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied.
After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of
classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical
implementation under real–time constraints.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1019&context=abe_eng_pubs

Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network

Field operations should be done in a manner that minimizes time and travels over the field surface and is coordinated with topographic land features Automated path planning can help to find the best coverage path so that the field operation costs can be minimized Intelligent algorithms are desired for both two-dimensional (2D) and three-dimensional (3D) terrain field coverage path planning The algorithm of generating an optimized full coverage pattern for a given 2D planar field by using boustrophedon paths has been investigated and reported before However, a great proportion of farms have rolling terrains, which have a considerable influence on the design of coverage paths Coverage path planning in 3D space has a great potential to further optimize field operations This work addressed four critical tasks: terrain modeling and representation, coverage cost analysis, terrain decomposition, and the development of optimized path searching algorithm The developed algorithms and methods have been successfully implemented and tested using 3D terrain maps of farm fields with various topographic features Each field was decomposed into subregions based on its terrain features A recommended “seed curve” based on a customized cost function was searched for each subregion, and parallel coverage paths were generated by offsetting the found “seed curve” toward its two sides until the whole region was completely covered Compared with the 2D planning results, the experimental results of 3D coverage path planning showed its superiority in reducing both headland turning cost and soil erosion cost On the tested fields, on average the 3D planning algorithm saved 103% on headland turning cost, 247% on soil erosion cost, 812% on skipped area cost, and 220% on the weighted sum of these costs, where their corresponding weights were 1, 1, and 05, respectively © 2011 Wiley Periodicals, Inc © 2011 Wiley Periodicals, Inc

Coverage path planning on three-dimensional terrain for arable farming

Recent advances in omics technologies have not been accompanied by equally efficient, cost-effective, and accurate phenotyping methods required to dissect the genetic architecture of complex traits. Even though high-throughput phenotyping platforms have been developed for controlled environments, field-based aerial and ground technologies have only been designed and deployed for short-stature crops. Therefore, we developed and tested Phenobot 1.0, an auto-steered and self-propelled field-based high-throughput phenotyping platform for tall dense canopy crops, such as sorghum (Sorghum bicolor). Phenobot 1.0 was equipped with laterally positioned and vertically stacked stereo RGB cameras. Images collected from 307 diverse sorghum lines were reconstructed in 3D for feature extraction. User interfaces were developed, and multiple algorithms were evaluated for their accuracy in estimating plant height and stem diameter. Tested feature extraction methods included the following: (1) User-interactive Individual Plant Height Extraction (UsIn-PHe) based on dense stereo three-dimensional reconstruction; (2) Automatic Hedge-based Plant Height Extraction (Auto-PHe) based on dense stereo 3D reconstruction; (3) User-interactive Dense Stereo Matching Stem Diameter Extraction; and (4) User-interactive Image Patch Stereo Matching Stem Diameter Extraction (IPaS-Di). Comparative genome-wide association analysis and ground-truth validation demonstrated that both UsIn-PHe and Auto-PHe were accurate methods to estimate plant height, while Auto-PHe had the additional advantage of being a completely automated process. For stem diameter, IPaS-Di generated the most accurate estimates of this biomass-related architectural trait. In summary, our technology was proven robust to obtain ground-based high-throughput plant architecture parameters of sorghum, a tall and densely planted crop species.

http://www.plantphysiol.org/content/plantphysiol/174/4/2008.full.pdf

A high-throughput, field-based phenotyping technology for tall biomass crops

This study was undertaken to develop machine vision-based weed detection technology for outdoor natural lighting conditions. Supervised color image segmentation using a binary-coded genetic algorithm (GA) identifying a region in Hue-Saturation-Intensity (HSI) color space (GAHSI) for outdoor field weed sensing was successfully implemented. Images from two extreme intensity lighting conditions, those under sunny and cloudy sky conditions, were mosaicked to explore the possibility of using GAHSI to locate a plant region in color space when these two extremes were
presented simultaneously. The GAHSI result provided evidence for the existence and separability of such a region. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding hand-segmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI achieved
equivalent performance.

/pdf/color-image-segmentation-with-genetic-algorithm-for-in-field-mtumo73opj.pdf

Lie Tang

Papers

Original paper: A vision based row detection system for sugar beet

Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network

Coverage path planning on three-dimensional terrain for arable farming

A high-throughput, field-based phenotyping technology for tall biomass crops

Color image segmentation with genetic algorithm for in-field weed sensing