Ramanbhai M. Patel

Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

This study evaluates the potential of decision tree classification algorithms for the classification of hyperspectral data, with the goal of discriminating between different growth scenarios in a cornfield. A comparison was also made between decision tree and artificial neural networks (ANNs) classification accuracies. In the summer of the year 2000, a two-factor field experiment representing different crop conditions was carried out. Corn was grown under four weed management strategies: no weed control, control of grasses, control of broadleaf weeds, and full weed control with nitrogen levels of 60, 120, and 250 N kg/ha. Hyperspectral data using a Compact Airborne Spectrographic Imager were acquired three times during the entire growing season. Decision tree technology was applied to classify different treatments based on the hyperspectral data. Various tree-growing mechanisms were used to improve the accuracy of classification. Misclassification rates of detecting all the combinations of different nitrogen and weed categories were 43, 32, and 40% for hyperspectral data sets obtained at the initial growth, the tasseling and the full maturity stages, respectively. However, satisfactory classification results were obtained when one factor (nitrogen or weed) was considered at a time. In this case, misclassification rates were only 22 and 18% for nitrogen and weeds, respectively, for the data obtained at the tasseling stage. Slightly better results were obtained by following the ANN approach. However, the advantage with the decision tree was the formulation of simple and clear classification rules. The highest accuracy was obtained for the data acquired at tasseling stage. The results indicate the potential of decision tree classification algorithms and ANN usage in the classification of hyperspectral data for crop condition assessment.

The effect of composting on the degradation of a veterinary pharmaceutical

Composting has been identified as a viable means of reducing the environmental impact of antibiotics in manure. The focus of the present study is the potential use of composting on the degradation of salinomycin in manure prior to its field application. Manure contaminated with salinomycin was collected from a poultry farm and adjusted to a C:N ratio of 25:1 with hay material. The manure was composted in three identical 120 L plastic containers, 0.95 m height x 0.40 m in diameter. The degradation potential for salinomycin was also ascertained under open heap conditions for comparison (control). Salinomycin was quantified on HPLC with a Charged Aerosol Detector, at an interval of every 3 days. The salinomycin level in the compost treatment decreased from 22 mg kg(-1) to 2 x 10(-5) microg kg(-1) over 38 days. The corresponding decrease in the control was from 27.5 mg kg(-1) to 24 microg kg(-1). The changes in pH, EC (dS m(-1)), temperature, total kjeldahl nitrogen (TKN), total potassium (TK), total phosphorus (TP) and carbon content in both the composting and the control samples were monitored and found to be different in compost as compared to the control. During the composting process, the loss of TKN was 36%, which was substantially lower than corresponding loss of 60% in the control. The loss of carbon was 10% during composting, whereas the loss in the control was 2%. In composting, the temperature modulated from 27 degrees C (initially) to a high of 62.8 degrees C (after 4 days), and then declined to 27.8 degrees C at the end of 38 days. On the basis of the results obtained in this study, it appears that the composting technique is effective in reducing salinomycin in manure.

ESTIMATION OF CROP BIOPHYSICAL PARAMETERS THROUGH AIRBORNE AND FIELD HYPERSPECTRAL REMOTE SENSING

The potential of airborne hyperspectral remote sensing in crop monitoring and estimation of various biophysical parameters was examined in this study. A field experiment, consisting of four weed control strategies (no weed control, broadleaf control, grass control, and full weed control) as the main plot effect, factorially combined with three nitrogen (N) fertilization rates (60, 120, and 250 N kg ha–1), and replicated four times, was conducted. Hyperspectral data in 72 narrow wavebands (409 to 947 nm) from a Compact Airborne Spectrographic Imager (CASI) sensor were acquired 30 days after planting, at tasseling, and at the fully mature stage. In addition, measurements were made concurrently on various crop physiological parameters: leaf greenness (SPAD readings), leaf area index (LAI), plant height, leaf nitrogen content, leaf chlorophyll content, and associated factors such as soil moisture. Regression models were generated to estimate crop biophysical parameters and yield, in terms of reflectance at one or more wavebands, using the maximum r2 improvement criterion. The models that best represented the data had five wavebands as independent variables. Coefficients of determination (r2) were generally greater than 0.9, when based on the spectral data taken at the tasseling stage. Results were improved when normalized difference vegetation indices (NDVI) were used rather than the five–waveband reflectance values. The wavebands at 701 nm and 839 nm were the most prevalent in the NDVI–based models.

Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn

A compact airborne spectrographic imager (CASI) was used to obtain images over a field that had been set up to study the effects of various nitrogen application rates and weed control on corn (Zea mays). The objective was to determine to what extent the reflectances obtained in the 72 visible and near-infrared (NIR) wavebands (from 409 to 947 nm) might be related to differences associated with combinations of weed control (none, full, grasses only or broadleafs only) and nitrogen application rate (60, 120 or 250 kg/ha). Plots were arranged in split-plot experiment in completely randomized design at the McGill University Research Farm on Macdonald Campus, Ste Anne de Bellevue, Que., Canada. Weeding treatments were assigned to the main-plot units, and nitrogen rates to the sub-plot units. Three flights were made during the growing season. Data were analyzed for each flight and each band separately, then regrouped into series of neighboring bands yielding identical analyses with respect to the significance of the main effects and interactions on reflectance. The results indicate that the reflectance of corn is significantly influenced (α=0.05) at certain wavelengths by the presence of weeds, the nitrogen rates and their interaction. The influence of weeds was most easily observed in the data from the second flight (August 5, 2000), about 9 weeks after planting. The nitrogen effect was detectable in all the three flights. Differences in response due to nitrogen stress were most evident at 498 nm and in the band at 671 nm. In these bands, differences due to nitrogen levels were observed at all growth stages, and the presence of weeds had no interactive effect. Differences in other regions, whether related to nitrogen, weeds or the combination of the two, appeared to be dependent on the growth stage. Furthermore, results comparable to those of the hyperspectral sensor were obtained when a multispectral sensor was simulated, indicating little advantage of using the former.

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données

Abstract Steep topography and land-use transformations in Himalayan watersheds have a major impact on hydrological characteristics and flow regimes, and greatly affect the perenniality and sustainability of water resources in the region. To identify the appropriate conservation measures in a watershed properly, and, in particular, to augment flow during lean periods, accurate estimation of streamflow is essential. Due to the complexity of rainfall—runoff relationships in hilly watersheds and non-availability of reliable data, process-based models have limited applicability. In this study, data-driven models, based upon the Multiple Adaptive Regression Splines (MARS) technique, were employed to predict streamflow (surface runoff, baseflow and total runoff) in three mid-Himalayan micro-watersheds. In addition, the effect of length of historical records on the performance of MARS models was critically evaluated. Though acceptable MARS models could be developed with a 2-year data set, their performance improved considerably with a 3-year data set. Various indicators of model performance, such as correlation coefficient, average deviation, average absolute deviation and modelling efficiency, showed significant improvement for simulation of surface runoff, baseflow and total flow. To further analyse the versatility and general applicability of the MARS approach, 2-year data sets were used to develop the model and test it on a third-year data set to assess its performance. The models simulated the surface runoff, baseflow and total flow reasonably well and can be reliably applied in ungauged small watersheds under identical agro-climatic settings.

USE OF AIRBORNE MULTI-SPECTRAL IMAGERY FOR WEED DETECTION IN FIELD CROPS

In this article, the potential of multi-spectral airborne remote sensing is evaluated for the detection of weed infestation in corn (Zea mays L.) and soybean (Glycine max.) crops. A field plot experiment was laid out at the Lods Agronomy Research Center of Macdonald Campus, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada. A multi-spectral image in 24 wavebands (475.12 nm to 910.01 nm wavelength range) was obtained using an airborne platform. Three weed treatments were selected to represent different weed conditions in corn and soybean, namely velvetleaf (Abutilon theophrasti Medic.), grasses, and mixed weeds. For the purpose of comparison, a treatment without weeds was also planted of each type of crop. Statistical analysis of radiance values recorded in different wavebands was performed to find the wavelength regions that were most useful for detecting different weed infestations. The results indicate that wavebands centered at 675.98 and 685.17 nm in the red region, and from 743.93 nm to 830.43 nm in the near-infrared, have good potential for distinguishing weeds in corn. For soybean, however, only one waveband (811.40 nm) was found to be useful. Efforts were also made to evaluate various ratios of radiance values recorded in red and near-infrared (NIR) wavebands for the detection of weeds. Much better results were obtained when ratios were used than with single wavebands. The results of this study will be helpful in selecting the most useful parts of the electromagnetic spectrum for the detection of weeds in corn and soybean fields.

Hyperspectral Image Classification to Detect Weed Infestations and Nitrogen Status in Corn

The potential of hyperspectral aerial imagery for the detection of weed infestation and nitrogen fertilization level in a corn (Zea mays L.) crop was evaluated. A Compact Airborne Spectrographic Imager (CASI) was used to acquire hyperspectral data over a field experiment laid out at the Lods Agronomy Research Centre of Macdonald Campus, McGill University, Quebec, Canada. Corn was grown under four weed management strategies (no weed control, control of grasses, control of broadleaf weeds, and full weed control) factorally combined with nitrogen fertilization rates of 60, 120, and 250 N kg/ha. The aerial image was acquired at the tasseling stage, which was 66 days after planting. For the classification of remote sensing imagery, various widely used supervised classification algorithms (maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped, and binary coding) and more sophisticated classification approaches (spectral angle mapper and linear spectral unmixing) were investigated. It was difficult to distinguish the combined effect of both weed and nitrogen treatments simultaneously. However, higher classification accuracies were obtained when only one factor, either weed or nitrogen treatment, was considered. With different classifiers, depending on the factors considered for the classification, accuracies ranged from 65.84% to 99.46%. No single classifier was found useful for all the conditions.

APPLICATION OF MARS IN SIMULATING PESTICIDE CONCENTRATIONS IN SOIL

Efforts were made to predict pesticide concentrations at three different depths in the soil profile, using models developed with Multivariate Adaptive Regression Splines (MARS), a regression analysis model. The models were developed with independently collected data from the Eugene F. Whelan Experimental Farm (Agriculture and Agri-Food Canada, Woodslee, Ontario, Canada) from 1992 to 1994. Data from 16 plots, subjected to four different tillage treatments and two different water table management practices, were used. The fate of three herbicides, namely atrazine [2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine], metribuzin [4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4- triazin-5(4H)-one], and metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(-2-methoxyl-1-methylethyl) acetamide], at three different soil depths were studied. The input variables for the models included Julian day, days after application of pesticide, measured herbicide concentrations, and cumulative figures for rainfall depth, air temperature, soil temperature, and potential evapotranspiration. Considering the limited size of the data set, a 10-fold cross-validation was performed to test and validate the model. Model predictions at the 0-10 cm depth were very close to the measured values, with model efficiencies varying from 83% to 99%. The predictions at the 10-15 cm depth generally varied from 33% to 83%, while the ones at the 15-20 cm depth were within 42% to 95%, with a few exceptions where the model predicted a single value, of the average observed concentrations. These results demonstrate that MARS was able to do a commendable job in simulating pesticide fate and transport in soil with limited data.

DEVELOPMENT AND FIELD VALIDATION OF THE PESTFATE MODEL IN SOUTHERN ONTARIO

A new pesticide movement model called PESTFATE (PESTicide Fate and Transport in Environment) has been developed by combining DRAINMOD, a well-known water table management model, and the pesticide submodel of PESTFADE. The pesticide sorption in the new model is based on two different techniques, namely, conventional mechanism and a new two-stage sorption method called Gamble kinetics. The model was validated by comparing the simulated midspan water table depths and atrazine [2-chloro-4-ethylamino-6-isopr opylamino-1, 3, 5-triazine] concentrations against an independently collected dataset from a research site in southern Ontario. The experimental field consisted of 16 plots with two different water table management and four different tillage practices, replicated twice. Only the plots with conventional tillage and controlled drainage were used in this study. The model performed well in predicting the daily water table depths. Although the intercept and slope of the regression between the observed and predicted water table depths were significantly different from 0 and 1, respectively, the model efficiencies for 1992, 1993, and 1994 were 28%, 81%, and 64%, respectively, which shows a better accuracy for the last two years of the study. The normalized standard errors were within 15% to 20%, which indicates good model performance for water flow simulation. However, the correlation between the observed and simulated pesticide concentrations with both the conventional and Gamble kinetics was poor. Moreover, the Gamble kinetics did not significantly improve pesticide simulations (P < 0.05) as compared to the conventional method. The experiments were conducted on Brookston clay loam soil, which is known for developing soil cracks and consequently preferential flow. This could be one of the reasons for poorer model performance, especially with the Gamble kinetics. This was not the case in previous studies, which reported better simulation results with the new sorption mechanism, so the model requires additional field testing before any concrete conclusions can be drawn about its performance. There is also a need to test the model with other pesticides.

Towards the Development of Salt-Tolerant Potato

Publisher Summary This chapter is focused on cultivated potato (S. tuberosum L.) and is not intended to encompass the multiplicity of salt effects on plants. The reader is referred to recent comprehensive review articles on sodium tolerance and salinity effects, potential biochemical indicators of salinity tolerance, cellular basis of salinity tolerance transport proteins and salt tolerance, screening methods for salinity tolerance, sodium tolerance and transport, comparative physiology of salt and water stress and many others. Salinity tolerance may be increased through conventional breeding efforts, tissue culture or molecular technologies. Improved or modified cultural and water management practices can enhance potato crop productivity. The application of these technologies to potato is reviewed, accomplishments to date are summarized, and possible strategies to accelerate the development of salinity tolerant potato are discussed. For comparison purposes, the global distribution of stress-affected soils, including saline soils, is shown. It is clear from these maps that most of the area under potato cultivation is in countries that are not overly affected by salinity. The exceptions are countries in southern and southeastern Asia, where coastal or inland salinity is more common. The chapter discusses whatever little that is known of water and fertilizer management for potato growing under salinity stress and how climatic conditions modulate salinity effects on potato. In 2006, there were no known commercially important S. tuberosum cultivars with outstanding salinity tolerance. For this reason, emphasis must be placed on improved water management techniques for potato production.