Greenhouse Robot Navigation Using KLT Feature Tracking for Visual Odometry

A visual odometer was developed for an autonomous greenhouse sprayer to estimate vehicle translation and rotation relative to the world coordinate system during navigation. Digital images were taken from a CCD camera mounted on the robot. 7 x 7 pixel features were selected in the image using the KLT algorithm (Csetverikov, 2004). Features were tracked from image to image by finding the best 7 x 7 pixel match of the feature within a 25 x 25 pixel search box. By analyzing the movement of these features, vehicle rotation and translation were estimated. Five features were tracked with the odometer. Tests were run to verify the visual odometer’s accuracy during translation, rotation, and on various surfaces. The visual odometer ran at an average of 10 Hz during experimentation. Translation tests of the odometer in a lab environment gave an average error of 4.85 cm for a 30.5 cm forward translation and 12.4 cm average error for a 305 cm translation. Rotation tests of the odometer in a lab environment gave an average error of 1° for a 45° rotation and an 8° error for a 180° rotation about the vehicle z-axis. Tests completed on concrete, sand, and gravel demonstrated adaptability of the odometer on different ground surfaces that are common in greenhouses. The visual odometer was successfully integrated into a visual navigation system for intersection navigation of an autonomous greenhouse sprayer

1983 opinions on farm policy from leading Missouri farmers

"In March 1983, 1,600 of Missouri's leading farmers were mailed a questionnaire asking them to state their opinions about current issues in farm policy. The answers from the 745 farmers who responded are summarized in this guide sheet.* The names of farmers sent questionnaires were selected randomly from the mailing list for the extension newsletter Economic and Marketing Information for Missouri Agriculture and from a list assembled by area extension specialists. The farmers included in the March 1983 survey are believed to be representative of Missouri's leading farmers, but not of all farmers. The survey cannot be regarded as reporting for all of Missouri agriculture."--First page.Meredith M. Burks and Harold F. Breimyer (Department of Agricultural Economics College of Agriculture)New 10/83/8

Wagon-Based Silage Yield Mapping System

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 7 (2005): Wagon-Based Silage Yield Mapping System by W. S. Lee, J. K. Schueller, T. F. Burk

A week-end off: the first extensive number-theoretical computation on the ENIAC

The first extensive number-theoretical computation run on the ENIAC, is reconstructed. The problem, computing the exponent of 2 modulo a prime, was set up on the ENIAC during a week-end in July 1946 by the number-theorist D.H. Lehmer, with help from his wife Emma and John Mauchly. Important aspects of the ENIAC's design are presented-and the reconstruction of the implementation of the problem on the ENIAC is discussed in its salient points

A Machine Vision Algorithm Combining Adaptive Segmentation and Shape Analysis for Orange Fruit Detection

 Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The two major challenges in the automation of harvesting are the recognition of the fruit and its detachment from the tree. This paper deals with fruit recognition and it presents the development of a machine vision algorithm for the recognition of orange fruits. The algorithm consists of segmentation, region labeling, size filtering, perimeter extraction and perimeter-based detection. In the segmentation of the fruit, the orange was enhanced by using the red chromaticity coefficient which enabled adaptive segmentation under variable outdoor illumination. The algorithm also included detection of fruits which are in clusters by using shape analysis techniques. Evaluation of the algorithm included images taken inside the canopy (varying lighting condition) and on the canopy surface. Results showed that more than 90% of the fruits visually recognized in the images were detected in the 110 images tested with a false detection rate of 4%. The proposed segmentation was able to deal with varying lighting condition and the perimeter-based detection method proved to be effective in detecting fruits in clusters. The development of this algorithm with its capability of detecting fruits in varying lighting condition and occlusion would enhance the overall performance of robotic fruit harvesting

Edge effect compensation for citrus canker lesion detection due to light source variation – a hyperspectral imaging application

The spread of citrus canker has become one of the most important challenges faced by Florida Fresh Market citrus is affecting the export of citrus fruits to several international markets including European countries.  Previous studies have shown that automated detection systems can help detect citrus canker infected fruit and could assist in eliminating the detected fruit from shipment to closed markets.  Most automated detection systems use some form of machine vision with artificial light sources.  However, when capturing images of spherical objects, non-uniform illumination results in an edge blackening effect resulting in higher misclassification rate.  The basic objective of this research was to implement a compensation algorithm to eliminate the edge effect when capturing hyperspectral image of citrus fruits.  The algorithm originally developed by Gomez et al. 2007, was adapted for the purpose of canker detection.  The image was corrected for spatial variations (flat field correction) caused by intensity of light source as well as geometrical variation caused by the spherical geometry of the citrus fruit.  In this study, the geometric correction was accomplished by constructing a 3-D digital elevation model (DEM) of the fruit from its 2-D image.  This DEM provided the geometric properties of the fruit X, Y, and Z coordinates which were exploited in the course of estimating the geometric correction factor for each pixel.  The corrected image portrayed a more uniform brightness of the citrus fruit surface throughout.  Tests were conducted on 10 orange samples (five marketable and five cankerous) to validate the results of the algorithm which demonstrated that the geometric correction resulted in uniform intensity of radiation throughout the fruit surface thus reducing the within class variation.   Keywords: edge effect compensation, hyperspectral imaging, canker, spatial correction, geometric correctio

Mineral Intake and Status of Cowʼs Milk Allergic Infants Consuming an Amino Acid-based Formula:

Data on the mineral status of infants with cow's milk allergy (CMA) consuming an amino acid-based formula (AAF) have not been published. The present study aims to assess mineral status of term infants age 0 to 8 months diagnosed with CMA receiving an AAF for 16 weeks. Serum concentrations of calcium, phosphorus, chloride, sodium, potassium, magnesium, and ferritin were determined in 82 subjects at baseline and in 66 subjects after 16 weeks on AAF using standard methods and evaluated against age-specific reference ranges. In addition to this, individual estimated energy and mineral intakes were compared to Adequate Intakes defined by the European Food Safety Authority and the US Institute of Medicine. The results of this study show that the AAF was effective in providing an adequate mineral status in infants with CMA. The vast majority of infants aged 0 to 6 months (formula only) and aged 6 to 12 months (formula and complementary foods) had adequate mineral intakes

Citrus black spot detection using hyperspectral image analysis

A recently discovered fungal disease called citrus black spot, is threatening the Florida citrus industry.  The fungal disease, which causes cosmetic lesions on the rind of the fruit and can cause a tree to drop its fruit prematurely, could possibly lead to a ban on sales of fresh Florida citrus in other citrus-producing states.  The objective of this research is to develop a multispectral imaging algorithm to detect citrus black spots based on hyperspectral image data.  Hyperspectral images of citrus fruits (Valencias) were collected in the wavelength range of 480 nm to 950 nm.  Five surface conditions were examined, citrus black spot, greasy spot, melanose, wind scar, and normal one.  The first part of the image analysis determined the optimal wavelengths using correlation analysis based on the wavelength ratio (l1/l2) and wavelength difference (l1 - l2).  Four wavelengths were identified, 493 nm, 629 nm, 713 nm, and 781 nm.  In the second part, pattern recognition approaches namely linear discriminant classifier and artificial neural networks were developed using the four selected wavelengths as the input.  Both pattern recognition approaches had an overall accuracy of 92%.  The detection accuracy was improved to 96% by using the NDVI band ratio method of 713 nm and 781 nm.  The multispectral image algorithm developed in this study haspotential to be adopted by a real-time multispectral imaging system for citrus black spot detection.     Keywords: activation energy, effective diffusivity, foam-mat drying, foam characteristics, modeling, Shrim

Simulation of Fixed– and Variable–Rate Application of Granular Materials

Research has shown that application errors exist with variable–rate technology (VRT) systems. Consequently, using prescription maps for economic and agronomic analyses can generate misleading results. The intent of this article was to develop and validate a spatial data model for generating “as–applied” maps to support the advancement of precision agriculture practices. Previous research modified ASAE Standard S341.2 to include a 2–D matrix of collection pans to assess fixed–rate and variable–rate (VR) deposition of granular fertilizers and agricultural lime from a spinner disc spreader. The “as–applied” spatial data model uses GIS functionality to generate “as–applied” surfaces by merging distribution patterns and a spatial field application file (FAF) into an “as–applied” surface representing the actual distribution of granular fertilizer or agricultural lime across a field. To validate the “as–applied” spatial data model, field studies were conducted by randomly placing collection pans across two fields. Murate of potash was then applied using a VR spinner spreader. The “as–applied” spatial data model was used to predict the amount of material each pan should have received. Comparisons were made between the actual and predicted application rates for two fields, with R2 values of 0.45 (field A) and 0.58 (field B) computed. However, R2 values of 0.16 (field A) and 0.21 (field B) were observed when comparing the actual application rates and prescription maps. These low R2 values indicated poor application by the spinner spreader but demonstrated that the “as–applied” model did a better job of representing the distribution of murate of potash when contrasted with the prescription maps. “As–applied” surfaces provide a means for evaluating fixed–rate and VR application of granular products while enhancing researchers’ ability to compare VR management approaches

Detection of Citrus Greening Using Microscopic Imaging

Citrus greening reduces fruit production and quality and will likely result in rapid tree decline and death. Because citrus greening symptoms are usually observed on the leaf surface, detection of citrus greening leaf symptoms can significantly aid in scouting for infected trees and managing the disease, thus reducing its spread and minimizing losses for citrus growers. This article presents the microscopic image analysis using color co-occurrence method to differentiate citrus leaves with eight conditions: greening blotchy mottle, green islands, iron deficiency, manganese deficiency, zinc deficiency, young flush leaves and normal mature leaves. Thirty-nine statistical features were extracted from transformed hue (H), saturation (S), and intensity (I) images using the color co-occurrence method for each leaf sample. The number of extracted texture features was reduced by a stepwise discriminant analysis. A discriminant function based on a measure of the generalized squared distance was used for classification. Three classification models were performed using (1) all leaf conditions, (2) all conditions except young flush leaves and (3) all conditions except young flush leaves and blotchy mottle. The three classification models obtained accuracies of 86.67 %, 95.60 % and 97.33 %, respectively. The overall performance was demonstrated in a confusion matrix. The model HSI_14, which used all conditions except young flush and blotchy mottle, resulted in the best accuracy for positive (96.67 %) and negative (97.5 %) symptoms