Herbicides applications could possibly be reduced if targeted. Targeting the applications requires prior identification and quantification of the weed population. This task could possibly be done by a weed scout robot. The ability to position a camera over the inter-row space of densely seeded crops will help to simplify the task of automatically quantifying weed infestations. As part of the development of an autonomous weed scout, a vision-based local positioning system for weed detection has been developed and tested in a laboratory setting. Four Line-detection algorithms have been tested and a robotic positioning device, or XYZtheta-table, was developed and tested. <p> The Line-detection algorithms were based respectively on a stripe analysis, a blob analysis, a linear regression and the Hough Transform. The last two also included an edge-detection step. Images of parallel line patterns representing crop rows were collected at different angles, with and without weed-simulating noise. The images were processed by the four programs. The ability of the programs to determine the angle of the rows and the location of an inter-row space centreline was evaluated in a laboratory setting. All algorithms behaved approximately the same when determining the rows angle in the noise-free images, with a mean error of 0.5°. In the same situation, all algorithms could find the centreline of an inter-row space within 2.7 mm. Generally, the mean errors increased when noise was added to the images, up to 1.1° and 8.5 mm for the Linear Regression algorithm. Specific dispersions of the weeds were identified as possible causes of increase of the error in noisy images. Because of its insensitivity to noise, the Stripe Analysis algorithm was considered the best overall. The fastest program was the Blob Analysis algorithm with a mean processing time of 0.35 s per image. Future work involves evaluation of the Line-detection algorithms with field images. <p>The XYZtheta-table consisted of rails allowing movement of a camera in the 3 orthogonal directions and of a rotational table that could rotate the camera about a vertical axis. The ability of the XYZtheta-table to accurately move the camera within the XY-space and rotate it at a desired angle was evaluated in a laboratory setting. The XYZtheta-table was able to move the camera within 7 mm of a target and to rotate it with a mean error of 0.07°. The positioning accuracy could be improved by simple mechanical modifications on the XYZtheta-table.

This study investigated the possibility of using ground-based remotely sensed hyperspectral observations with a special emphasis on detection of water, weed and nitrogen stresses contributing towards in-season decision support for precision crop management (PCM). / A three factor split-split-plot experiment, with four randomized blocks as replicates, was established during the growing seasons of 2003 and 2004. Corn (Zea mays L.) hybrid DKC42-22 was grown because this hybrid is a good performer on light soils in Quebec. There were twelve 12 x 12m plots in a block (one replication per treatment per block) and the total number of plots was 48. Water stress was the main factor in the experiment. A drip irrigation system was laid out and each block was split into irrigated and non-irrigated halves. The second main factor of the experiment was weeds with two levels i.e. full weed control and no weed control. Weed treatments were assigned randomly by further splitting the irrigated and non-irrigated sub-blocks into two halves. Each of the weed treatments was furthermore split into three equal sub-sub-plots for nitrogen treatments (third factor of the experiment). Nitrogen was applied at three levels i.e. 50, 150 and 250 kg N ha-1 (Quebec norm is between 120-160 kg N ha-1). / The hyperspectral data were recorded (spectral resolution = 1 nm) mid-day (between 1000 and 1400 hours) with a FieldSpec FR spectroradiometer over a spectral range of 400-2500 run at three growth stages namely: early growth, tasseling and full maturity, in each of the growing season. / There are two major original contributions in this thesis: First is the development of a hyperspectral data analysis procedure for separating visible (400-700 nm), near-infrared (700-1300 nm) and mid-infrared (1300-2500 nm) regions of the spectrum for use in discriminant analysis procedure. In addition, of all the spectral band-widths analyzed, seven waveband-aggregates were identified using STEPDISC procedure, which were the most effective for classifying combined water, weed, and nitrogen stress. The second contribution is the successful classification of hyperspectral observations acquired over an agricultural field, using three innovative artificial intelligence approaches; support vector machines (SVM), genetic algorithms (GA) and decision tree (DT) algorithms. These AI approaches were used to evaluate a combined effect of water, weed and nitrogen stresses in corn and of all the three AI approaches used, SVM produced the best results (overall accuracy ranging from 88% to 100%). / The general conclusion is that the conventional statistical and artificial intelligence techniques used in this study are all useful for quickly mapping combined affects of irrigation, weed and nitrogen stresses (with overall accuracies ranging from 76% to 100%). These approaches have strong potential and are of great benefit to those investigating the in-season impact of irrigation, weed and nitrogen management for corn crop production and other environment related challenges.

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Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: A collaboration between the Katholieke Universiteit van Leuven (KUL) and Stellenbosch University (SU), resulted in the development of a satellite based platform for use in agricultural sensing applications. This will primarily serve as a test platform for a digitally beam-steerable antenna array (SAA) that was developed by KUL. SU developed all flight - and ground station based hardware and software, enabling ground to flight communications and interfacing with the KUL SAA. Although most components had already been completed at the start of this M:Sc:Eng: project, final systems integration was still unfinished. Modules necessary for communication were also outstanding. This project implemented an automatic repeat and request (ARQ) strategy for reliable file transfer across the wireless link. Channel coding has also been implemented on a field programmable gate array (FPGA). This layer includes an advanced forward error correction (FEC) scheme i.e. a low-density parity-check (LDPC), which outperforms traditional FEC techniques. A flexible architecture for channel coding has been designed that allows speed and complexity trade-offs on the FPGA. All components have successfully been implemented, tested and integrated. Simulations of LDPC on the FPGA have been shown to provide excellent error correcting performance. The prototype has been completed and recently successfully demonstrated at KUL. Data has been reliably transferred between the satellite platform and a ground station, during this event. / AFRIKAANSE OPSOMMING: Tydens ’n samewerkingsooreenkoms tussen die Katholieke Universiteit van Leuven (KUL) en die Universiteit van Stellenbosch (US) is ’n satelliet stelsel ontwikkel vir sensor-netwerk toepassings in die landbou bedryf. Hierdie stelsel sal hoofsaaklik dien as ’n toetsmedium vir ’n digitaal stuurbare antenna (SAA) wat deur KUL ontwikkel is. Die US het alle hardeware en sagteware komponente ontwikkel om kommunikasie d.m.v die SAA tussen die satelliet en ’n grondstasie te bewerkstellig. Sedert die begin van hierdie M:Sc:Ing: projek was die meeste komponente alreeds ontwikkel en geïmplementeer, maar finale stelselsintegrasie moes nog voltooi word. Modules wat kommunikasie sou bewerkstellig was ook nog uistaande. Hierdie projek het ’n ARQ protokol geïmplementeer wat data betroubaar tussen die satelliet en ’n grondstasie kon oordra. Kanaalkodering is ook op ’n veld programmeerbare hekskikking (FPGA) geïmplementeer. ’n Gevorderde foutkorrigeringstelsel, naamlik ’n lae digtheids pariteit toetskode (LDPC), wat tradisionele foutkorrigeringstelsels se doeltreffendheid oortref, word op hierdie FPGA geïmplementeer. ’n Kanaalkoderingsargitektuur is ook ontwikkel om die verwerkingspoed van data en die hoeveelheid FPGA logika wat gebruik word, teenoor mekaar op te weeg. Alle komponente is suksesvol geïmplementeer, getoets en geïntegreer met die hele stelsel. Simulasies van LDPC op die FPGA het uistekende foutkorrigeringsresultate gelewer. ’n Werkende prototipe is onlangs voltooi en suksesvol gedemonstreer by KUL. Betroubare data oordrag tussen die satelliet en die grondstasie is tydens hierdie demonstrasie bevestig.