Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

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This paper presents the development of prototype system for monitoring and computing greenhouse gases (GHG) with Unmanned Aerial Vehicle (UAV) deployment for collecting data for different nodes. This system is based on wireless ZigBee technology that transfers the data wirelessly to UAV, which function is as a router, from which data is sent again to a data logger. An ATMEGA328P microcontroller is used to compute parameters such as CO 2 , O 2 , temperature and humidity. All the environmental parameters are measured on real time and are being stored in Secured Digital (SD) card for every 30 seconds interval. The data is collected at Engineering Campus, Universiti Sains Malaysia in Nibong Tebal, Pulau Pinang, Malaysia using UAV at several nodes. The results show that the system is able to trace and record CO 2 , O 2 , temperature and humidity level, which are important parameters for studies related to global warming.

Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation by exploiting the nature of probability math. SC was proposed in the 1960s when binary computing was expensive. However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation. Although not all computing functions can translate to the SC domain, several useful function blocks related to the CNN algorithm had been proposed and tested by researchers. An evolution of CNN, namely, binarised neural network, had also gained attention in the edge computing due to its compactness and computing efficiency. This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure and then compare the advantages and disadvantages amongst different SC methods. Finally, we conclude the overview of SC in CNN and make suggestions for widespread implementation.

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The growing number of sales and demands for output in the production line has led to an increase in difficulties in production management. This paper introduces the use of Radio Frequency Identification (RFID) or Automatic Identification (Auto ID) technology at 2.4 GHz frequency to improve the production line management system in the electronics industry. A case study approach was set up, and a test was run in a real industry production line environment using RFID-based systems, namely, passive reader (PR) and passive and active reader (PAR) systems. A comparison between the current system (i.e., barcode-based system) and the RFID-based system in production line showed that PR had 94.7 % efficiency while PAR had 90 % efficiency. The set-up was used to study in depth the functionality, advantages, compatibility, complexity, extendibility, and data output of the developed system. The in-depth test run studied the experience and identified the challenges that will be faced in the development and implementation of a wireless RFIDbased system.