Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower
generation. This research describes activities that lead to development of fluvial particle characterization that includes
detection of biological and non-biological particles and shape characterization using Image Processing and Artificial
Neural Network (ANN).
Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of
630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found
in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random
samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from
each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows
and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first
validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This
network classified biological and non biological particles.
Development of new non-destructive technique to characterize biological and non-biological particles from fluvial
sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model
for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and
for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at
Kathmandu University in Nepal.
Erosion of hydro turbine components through sand laden river is one of the biggest problems in Himalayas. Even
with sediment trapping systems, complete removal of fine sediment from water is impossible and uneconomical;
hence most of the turbine components in Himalayan Rivers are exposed to sand laden water and subject to erode.
Pelton bucket which are being wildly used in different hydropower generation plant undergoes erosion on the
continuous presence of sand particles in water. The subsequent erosion causes increase in splitter thickness, which is
supposed to be theoretically zero. This increase in splitter thickness gives rise to back hitting of water followed by
decrease in turbine efficiency. This paper describes the process of measurement of sharp edges like bucket tip using
digital image processing. Image of each bucket is captured and allowed to run for 72 hours; sand concentration in
water hitting the bucket is closely controlled and monitored. Later, the image of the test bucket is taken in the same
The process is repeated for 10 times. In this paper digital image processing which encompasses processes that
performs image enhancement in both spatial and frequency domain. In addition, the processes that extract attributes
from images, up to and including the measurement of splitter's tip. Processing of image has been done in MATLAB
6.5 platform. The result shows that quantitative measurement of edge erosion of sharp edges could accurately be
detected and the erosion profile could be generated using image processing technique.
Sand, chemical waste, microbes and other solid materials flowing with the water bodies are of great significance to us as
they cause substantial impact to different sectors including drinking water management, hydropower generation,
irrigation, aquatic life preservation and various other socio-ecological factors. Such particles can't completely be avoided
due to the high cost of construction and maintenance of the waste-treatment methods. A detailed understanding of solid
particles in surface water system can have benefit in effective, economic, environmental and social management of water
resources. This paper describes an automated system of fluvial particle characterization based on spectral image
processing that lead to the development of devices for monitoring flowing particles in river. Previous research in
coherent field has shown that it is possible to automatically classify shapes and sizes of solid particles ranging from 300-400 μm using artificial neural networks (ANN) and image processing. Computer facilitated with hyper spectral and multi
spectral images using ANN can further classify fluvial materials into organic, inorganic, biodegradable, bio non
degradable and microbes. This makes the method attractive for real time monitoring of particles, sand and microorganism
in water bodies at strategic locations. Continuous monitoring can be used to determine the effect of socio-economic
activities in upstream rivers, or to monitor solid waste disposal from treatment plants and industries or to monitor erosive
characteristic of sand and its contribution to degradation of efficiency of hydropower plant or to identify microorganism,
calculate their population and study the impact of their presence. Such system can also be used to characterize fluvial
particles for planning effective utilization of water resources in micro-mega hydropower plant, irrigation, aquatic life
Sand deposition is the major problem of Nepalese rivers and it causes substantial impact to different sectors including hydropower generation, natural resource management, and many others. Due to the typical nature of soil and sand of Nepalese mountains it has almost become impossible to predict and manage the upcoming natural disasters and hazards. Sand deposition in rivers affect landslides, aquatic life of rives, environmental disorders and many others. Sedimentation causes not only disasters but also reduces the overall efficiency of hydropower generation units as well. A systematic approach to the problem has been identified in this work. Sand particles are collected from the erosion sensitive power plants and its digital images have been acquired. Software has been developed on
MATLAB 6.5 platform to extract the exact shape of sand particles collected. These shapes have further been analyzed by artificial neural network. This network has been first trained for the known input and known output. After that it is trained for unknown input and known output. Finally these networks can recognize any shape given to it and gives the shape which is nearest to the seven predefined shape. The software is trained for seven types of shapes with shape number 1 to 7 in increasing number of sharp edges. The shape with shape number seven is having large number of sharp edges and considered as most erosive where as shape with shape number one is having round edges and considered as least erosive.
Opto-electronic methods represent a potential to identify the presence of insect activities on or within agricultural
commodities. Such measurements may detect actual insect presence or indirect secondary changes in the product
resulting from past or present insect activities. Preliminary imaging studies have demonstrated some unique spectral
characteristics of insect larvae on cherries. A detailed study on spectral characteristics of healthy and infested tart cherry
tissue with and without larvae (Plum Curculio) was conducted for reflectance, transmittance and interactance modes for
each of UV and visible/NIR light sources.
The intensity of transmitted UV signals through the tart cherry was found to be weak; however, the spectral properties
of UV light in reflectance mode has revealed some typical characteristics of larvae on healthy and infested tissue. The
larvae on tissue were found to exhibit UV induced fluorescence signals in the range of 400-700 nm. Multi spectral
imaging of the halved tart cherry has also corroborated this particular behavior of plum curculio larvae. The gray scale
subtraction between corresponding pixels in these multi-spectral images has helped to locate the larvae precisely on the
tart cherry tissue background, which otherwise was inseparable.
The spectral characteristics of visible/NIR energy in transmittance and reflectance mode are capable of estimating the
secondary effect of infestation in tart cherry tissue. The study has shown the shifting in peaks of reflected and
transmitted signals from healthy and infested tissues and coincides with the concept of browning of tissue at cell level as
a process of infestation.
Interactance study has been carried out to study the possibility of coupling opto-electronic devices with the existing
pitting process. The shifting of peaks has been observed for the normalized intensity of healthy and infested tissues. The
study has been able to establish the inherent spectral characteristic of these tissues. It was found that there existed
promising futuristic possibilities to use opto-electronic sensing to estimate the degree of secondary effect of insect
activities within the tissue.
Multispectral imaging in reflectance and fluorescence modes combined with neural network analysis was used to
classify various types of apple disorder from three apple varieties (Honey Crisp, Red Cort, and Red Delicious). Eighteen
images from a combination of filter sets and three different imaging modes (reflectance, visible light induced
fluorescence, and UV induced fluorescence) were acquired for each apple sample as a base for pixel-level classification
into normal or disorder tissue. Two classification schemes, a 2-class and a multiple class, were developed and tested in
this study. In the 2-class scheme, pixels were categorized into normal or disorder tissue, whereas in the multiple class
scheme, pixels were categorized into normal, bitter pit, black rot, decay, soft scald, and superficial scald tissues. Results
indicate that single variety training under the 2-class scheme yielded highest accuracy with total accuracy of 95, 97, and
100 % for Honey Crisp, Red Cort, or Red Delicious respectively. In the multiple-class scheme, the classification
accuracy of Honey Crisp apple for normal, bitter pit, black rot, decay, and soft scald tissue was 94, 93, 97, 97, and 94 %
respectively. Through variable selection analysis, in the 2-class scheme, fluorescence models yielded higher total
classification accuracy compared to reflection models. For Red Cort and Red Delicious, models with only FUV yield
more than 95% classification accuracy, demonstrating a potential of fluorescence to detect superficial scald. Several
important wavelengths, including 680, 740, 905 and 940 nm, were identified from the filter combination analysis. The
results indicate the potential of this technique to accurately recognize different types of disorder on apple.