Don Bosco Institute of Technology, Mumbai

About: Don Bosco Institute of Technology, Mumbai is a based out in . It is known for research contribution in the topics: Deep learning & Epoxy. The organization has 599 authors who have published 304 publications receiving 1367 citations.

Rice crop yield prediction in India using support vector machines

Abstract: Food production in India is largely dependent on cereal crops including rice, wheat and various pulses. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Variability in seasonal climate conditions can have detrimental effect, with incidents of drought reducing production. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. This paper presents the review on use of such machine learning technique for Indian rice cropping areas. This paper discusses the experimental results obtained by applying SMO classifier using the WEKA tool on the dataset of 27 districts of Maharashtra state, India. The dataset considered for the rice crop yield prediction was sourced from publicly available Indian Government records. The parameters considered for the study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.

Home automation using Internet of Things

Abstract: This paper represents the controlling and monitoring of the home environment using wireless sensors. An automated system is represented which can be used for controlling the lights and fans. A computer screen or a Smartphone can be used to monitor the Main control Unit(MCU). A very simplistic Graphical User Interface (GUI) is used which can be easily understandable for the target user. To make the data easily available to the user, this GUI will be implemented on the webpage.

Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA

Abstract: Image processing has applications in real time embedded systems. Real time image processing requires processing on large data of image pixels in a stipulated time. Reconfigurable device such as FPGAs can be program to process on large image data and required processing time on image can be reduced by deploying parallelism, pipelining techniques in algorithm. Edge detection is very basic tool used in many image processing. Robert, Prewitt, Sobel edge detection are gradient based edge detection methods used to find edge pixels in an image. This paper presents comparisons of Robert, Prewitt, Sobel operators based edge detection techniques for real time uses. Edge detection algorithms are written with the help of hardware descriptive language VHDL. Xilinx ISE Design Suite-13 and MATLAB software platforms are used for simulation purpose. This paper focus on edge detection of gray scale image.