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Pest Prediction in Rice using IoT and Feed Forward Neural Network
( Muhammad Salman Latif ),( Rafaqat Kazmi ),( Nadia Khan ),( Rizwan Majeed ),( Sunnia Ikram ),( Malik Muhammad Ali-shahid ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1
Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5<sup>th</sup> of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2<sup>nd</sup> largest crop being produced and 3<sup>rd</sup> largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer’s palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.
Faheem Shah,Naeemullah,Tasneem Gul Kazi,Rafaqat Ali Khan,Murtaza Sayed,Hassan Imran Afridi,Khizar Hussain Shah,Jan Nisar 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.48 No.-
A novel restricted access sorbents based micro solid phase extraction (RAS-mSPE) for the extraction ofheavy metal from biological samples has been developed. Cadmium (Cd) and manganese (Mn) weredirectly extracted skipping the tedious, time consuming and expensive sample preparation stepexcluding all of the existing proteins. Sorbent’s (activated carbon cloths) surface was modified withbovine serum albumin through glutaraldehyde to make restricted access sorbent (RAS). Differentvariables affecting the extraction efficiency were selected for optimization. The limits of detectionobtained for Cd and Mn were 0.252 and 0.214 mg L 1, respectively. Analyte recoveries in fortified humanwhole blood serum and milk samples were found in the range of 90.3–103.9%. The procedure waseffectively used for Cd and Mn extraction in real samples devoid of any pretreatment step.