-The nature of wireless sensor networks necessitates specific design requirement, of which energy efficiency is paramount. One of the important parameter in ensuring a good WSN system is the routing protocol. The energy source in WSN is irreplaceable and their lifetime is limited. Thus, an energy efficient routing protocol is inevitable in WSN. This project present the development of an energy efficient routing protocol which consumes significantly less power compared to existing routing protocol for Wireless Sensor Network. The design was developed based on Low Energy Adaptive Clustering Hierarchy (LEACH) routing protocol, intended to reduce the overall energy consumption. Clustering is an energy efficient and scalable way to organize the WSN. The main objective is to minimize the energy dissipation of each sensor node and reduces the energy dissipation for the whole network. Stable Cluster Head Election (SCHE) analyzes the cluster head selection to find the optimal probability of becoming a cluster head. Simulation of Matlab shows that this design reduces communication energy by as much as 95% compared to LEACH, due to stable cluster head election.

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This paper presents a statistical study for rubber seed clones classification. There are five types of clones from the same series of rubber seed being used as samples in this work which are the PB360, RRIM2009, RRIM2011, RRIM2016 and RRIM2025. The main objective is to identify significant features based on reflectance indices of both lateral and dorsal of the rubber seed surfaces from the application of ZEISS spectrometer instrument. The instrument measures the percentage of reflectance with respect to intensity of safe radiation light being reflected from the seed surface. Empirical analysis is done using SPSS software in order to identify discrimination between the clones. From the observed error plots and one-way ANOVA measurements, it is shown that reflectance indices of lateral surface can be used to recognize significantly the RRIM2009 from the other rubber seed clones.

In this paper, the classification of five types of rubber leaf disease by using the spectrometer and SPSS are presented. There are five of leaf disease that have been used as samples which are Oidium secondary leaf fall, Fusicoccum Leaf Blight, Bird eye's spot and Anthracnose. The reflectance of the infected leaves sample is measured by using MCS600 Carl Zeiss spectrometer. Besides, Aspect Plus, a universal spectroscopy program from Zeiss manages to measure the spectral regions of the leaves sample. Further analysis and justification are completed by using approximate statistical tools from SPSS. The results obtained show that there are strong evidences that these diseases can be discriminated from each other using a spectrometer.

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