Tea is a popular beverage all around the world, and in Bangladesh the cultivation of tea plays a vital role. Many diseases affect the proper growth of tea leaves leading to its reduction, thus hindering of the production of tea. However, if the disease is identified at an early age it would solve all the above mentioned problems through the application of appropriate treatment, or through the pruning of the diseased leaves to prevent further spread of the disease. To solve this problem image processing is the best option to detect and diagnose the disease. The main goal of this research is to develop an image processing system that can identify and classify the two most widespread tea leaf diseases in Bangladesh, namely brown blight disease and the algal leaf disease, from a healthy leaf. Disease identification is the first step; there are many methods that have been used for identifying the leaf disease. In this paper, Support Vector Machine classifier (SVM) is used to recognize the diseases. Eleven features are analyzed during the classification. These features are then used to find the most suitable match for the disease (or normality) every time an image is uploaded into the SVM database. When a new picture is uploaded into the system the most suitable match is found and the disease is recognized. The approach is novel since the number of features compared by the SVM classifier is reduced by three features compared to previous researches, without adversely sacrificing the success rate of the classifier, which retains an accuracy of more than 90%. This also speeds up the identification process, with each leaf image taking 300ms less processing time compared to previous research using SVM, thus ensuring a greater number of leaves can be processed in a given time frame. The proposed solution increases in efficiency of the detection, identification, and classification process will enable the tea industry in Bangladesh to become more competitive globally, by reducing the losses suffered due to diseases of the leaf, and thus increasing the overall tea production rate.

In this research work, a novel design of octagonal cladding with two elliptical cores in photonic crystal fiber (O-PCF) is studied for application in the terahertz (THz) frequency regime. There exists five layers octagonal figure in circular air holes (CAH) of cladding region with two elliptical shapes of core area are proposed in this research work. The O-PCF features of the fiber have been explored using full vector finite element method (FEM) with perfectly match layer (PML) boundary condition that have been used to confine the light inside the core region properly. Some supplementary significant fiber features for THz signal spread with low confinement loss, high core power fraction, scattering loss and single-mode PCF circumstances of the fiber have also been considered. Simulated results exhibition compares to the low effective material loss (EML) of 0.0153 cm ⁻¹ , larger effective area of 5.95 × 10 ^–8 m ² , core power fraction of 79%, a low confinement loss and scattering loss such as 3.36 × 10 ^–14 dB m ⁻¹ and 1.25 × 10 ^–10 dB km ⁻¹ respectively at 1 THz frequency regime. To shorten design and smooth fabrication, only circular shaped air holes have been employed. Due to its promising characteristics, the proposed SM-OPCF waveguide provides efficient transmission of broadband terahertz signals. The suggested excellent designed of O-PCF can be used mostly for in nano-optics, biomedical signal processing, optical amplification, SONET and others communication areas.