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Fariddin, Shaik Baba
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Internet of things based smart agriculture using K-nearest neighbor for enhancing the crop yield Dasari, Kalyankumar; Kharde, Mukund Ramdas; Maddileti, Kuruva; Pasupuleti, Venkat Rao; Ram, Mylavarapu Kalyan; Sujana, Challapalli; Komali, Govindu; Fariddin, Shaik Baba
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp436-445

Abstract

Agriculture is one of the major occupations in India and is one of the significant contributors to the economy of India. The agriculture plays a vital role in country gross domestic product (GDP) and is also part of civilization. The production of crop influences the economies of countries. However, still the agriculture filed stands technologically backward. In addition, the lack of favourable weather conditions might result loss of crops yields. The farmers need awareness about their soils, timely weather updates and techniques to improve their soil for growing healthy crops. Hence it is essential to develop a system which can technologically support the farmers for suggesting the crop and improving crop yields. With the development of electronics, researchers have been developed many applications and micro controllerbased systems to do agricultural operations. The internet of things (IoT) has opened many opportunities to design and implements a smart agriculture system and machine learning (ML) algorithm can help to obtain accurate performance. Hence, in this analysis, IoT based smart agriculture using K-nearest neighbor (KNN) for enhancing the crop yields is presented. With the combination of IoT and ML algorithm this system is designed which integrates primary agriculture operations such as recommendation of crops, automated watering and fertilizers recommendation.
Design and analysis for robotic arm position for automatic electric vehicle Kharde, Mukund Ramdas; Kalam, Sayyad Abdul; Teku, Kalyani; Reddy, Thumu Srinivas; Satya Srinivas, Gollapalli Veera; Kollamudi, Pavani; Fariddin, Shaik Baba; Kumar, Gopinati Pranay
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1517-1526

Abstract

Nowadays electric vehicles (EV) utilization is increasing. Because of charging issues, EVs are troubling people at the time of the journey because of the lack of charging stations. Therefore, to overcome these issues, robotic arm position for automatic electric vehicle is introduced in this analysis. This vehicle is operated through solar, so charging issues are overcome. The robotic arm position for automatic electric vehicle is fully automated by 4 infrared radiation (IR) sensors, which are placed in variations, back and other sides with particular speed limit variations, so that accidents can be avoided. The Flux in hand gloves can operate without manual operation while driver is sleeping. This analysis uses Raspberry Pi, python software with machine learning (ML) algorithm (support vector machine). Hence, this robotic arm position for automatic electric vehicle shows better results in terms of charging issues, accident ratio and driver presence.
Co-Authors Dasari, Kalyankumar Kalam, Sayyad Abdul Kharde, Mukund Ramdas Kollamudi, Pavani Komali, Govindu Kumar, Gopinati Pranay Maddileti, Kuruva Pasupuleti, Venkat Rao Ram, Mylavarapu Kalyan Reddy, Thumu Srinivas Satya Srinivas, Gollapalli Veera Sujana, Challapalli Teku, Kalyani