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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This study investigated the possibility of using data, acquired from airborne multi-spectral or hyper-spectral sensors, to detect nitrogen status and presence of weeds in crops; with the ultimate aim of contributing towards the development of a decision support system for precision crop management (PCM). / A 24-waveband (spectrum range 475 to 910 nm) multi-spectral sensor was used to detect weeds in corn (Zea mays L.) and soybean ( Glycine max (L.) Merr.) in 1999. Analysis of variance (ANOVA), followed by Scheffe's test, were used to determine which wavebands displayed significant differences in aerial spectral data due to weed treatments. It was found that the radiance values were mainly indicative of the contribution of weeds to the total vegetation cover in various plots, rather than indicative of changes in radiance of the crops themselves, or of differences in radiance between the weed populations and the crop species. / In the year 2000, a 72-waveband (spectrum range 407 to 949 nm) hyperspectral sensor was used to detect weeds in corn gown at three nitrogen levels (60, 120 and 250 kg N/ha). The weed treatments were: no control of weeds, control of grasses, control of broadleaved weeds and control of all weeds. Imagery was acquired at the early growth, tassel, and fully-mature stages of corn. Hyper-spectral measurements were also taken with a 512-waveband field spectroradiometer (spectrum range 270 to 1072 nm). Measurements were also carried out on crop physiological and associated parameters. ANOVA and contrast analyses indicated that there were significant (alpha = 0.05) differences in reflectance at certain wavebands, due to weed control strategies and nitrogen application rates. Weed controls were best distinguished at tassel stage. Nitrogen levels were most closely related to reflectance, at 498 nm and 671 nm, in the aerial data set. Differences in other wavebands, whether related to nitrogen or weeds, appeared to be dependent on the growth stage. Better results were obtained from aerial than ground-based spectral data. / Regression models, representing crop biophysical parameters and yield in terms of reflectance, at one or more wavebands, were developed using the maximum r2 criterion. The coefficients of determination (r 2) were generally greater than 0.7 when models were based on spectral data obtained at the tassel stage. Models based on normalized difference vegetation indices (NDVI) were more reliable at estimating the validation data sets than were the reflectance models. The wavebands at 701 nm and 839 nm were the most prevalent in these models. / Decision trees, artificial neural networks (ANNs), and seven other classifiers were used to classify spectral data into the weed and nitrogen treatment categories. Success rates for validation data were lower than 68% (mediocre) when training was done for all treatment categories, but good to excellent (up to 99% success) for classification into levels of one or the other treatment (i.e. weed or nitrogen) and also classification into pairs of levels within one treatment. Not one classifier was determined best for all situations. / The results of the study suggested that spectral data acquired from airborne platforms can provide vital information on weed presence and nitrogen levels in cornfields, which might then be used effectively in the development of PCM systems.

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