Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Robotics and computer-integrated manufacturing

Hydrogen is present (IbundanLly in biological tis~uc. Protons posse~s two fundamental physical properlie~, (i) spin (L) LInd (ii) a small magnetic movement ( 11). Therefore the ,Homic nuclei or hydrogcn behave like tiny spi nn ing magnets. Our eart h is al~o (l n examplt: of such a magnet ic spinning top. When they a re placed in a sIalic magnetic Ilcld (Fig. I), the nudcl align themselves parallel to Ihe applied magnetic field. Because they spin. the prolons expericnce a couple like a gyro~cope and ['Hecess (spin like a lap) aboul the dirl:cLion of lhe magnetic field B a indic;\ted in Fig. 2. j lISI as a spin.ni ng top precesse a bout the vert ical gravitat.ional field. This precession occurs at a definite rcquency proportional to the magnetic field intcn.~ity B. This frequency is a resonant frequency. known as the Larmor frequency which can be calculaled by the following equation. f := 'Y B ( I )

Nuclear magnetic resonance imaging

An instrument, based on near infrared (NIR) reflectance techniques, is described which is capable of determining nondestructively the percent soluble solids (SS) in whole honeydew melons. It uses tilting-interference-filter technology for wavelength scanning and a silicon detector with preamplifier for sensing radiation which has passed through inner melon flesh. The standard error of calibration was 0.82 SS when compared with standard laboratory refractometer measurements.

Instrument for nondestructive measurement of soluble solids in honeydew melons.

This paper explains how image processing techniques and Machine Learning algorithms were used, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Random Forest; and Deep Learning ś technique Convolutional Neural Network (CNN) was also used so we can determine which is the best algorithm for the construction of a recognition model that detects whether a blueberry plant is being affected by a disease or pest, or if it is healthy. The images were processed with different filters such as medianBlur and gaussianblur for the elimination of noise, the add Weighted filter was used for the enhancement of details in the images. The images were compiled by the authors of this work, since there was no accessible database of this specific kind of fruit, for which we visited Valle and Pampa farm so we could take pictures of different blueberry leaves, labeled in three different tags: diseased, plagued and healthy. The extraction of characteristics was done with algorithms such as HOG (Histogram of oriented gradients) and LBP (Local binary patterns), both normalized and not normalized. The results of the model showed an 84% accuracy index using Deep Learning, this model was able to classify whether the blueberry plant was being affected or not. The result of this work provides a solution to a constant problem in the agricultural sector that affects the production of blueberries, because pests as well as diseases are constant problems in this sector.

/pdf/diseases-detection-in-blueberry-leaves-using-computer-vision-1qvgel4mre.pdf

Diseases Detection in Blueberry Leaves using Computer Vision and Machine Learning Techniques

During storage and shipping of blueberries visible and internal fruit damages can lead to rejection at the destination. High maturity variability of blueberry, make it valuable to study non-destructive methodologies for their internal and external assessment during the postharvest. This would improve the quality of blueberries shipped overseas. This thesis has two principal themes: the external quality evaluation of blueberries using pattern recognition techniques with visible images and, the assessment of internal quality using hyperspectral images. For external quality evaluation, the automatic detection of distinctive blueberry orientations (Stem end and Calyx end) and defective fruit fungally decayed, shriveled and mechanically damaged by impacts or compressed-, were achieved with statistical pattern recognition methods. First, the four-classes (including control berries without damage) of harvested blueberries were imaged to extract color and geometrical features. Features were then selected using sequential forward selection for use by classifiers. Finally, results were validated with external 10-fold cross validation. Using linear discriminant analysis, support vector machine and a probabilistic neural network was able to distinguish the blueberries‘ orientation in 96.5 % of the cases. The classifiers achieved average performances of 98.3 %, 96.7 %, and 93.3 % for fungally decayed, shriveled, and mechanically damaged blueberries.

Assessment of external and internal quality of blueberries using images.

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Assessment of external and internal quality of blueberries using images.