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IOT Based Vegetable Crop Pest Prediction Using Smart Farming

Author(s)
Rana Muhammad Saleem Department of Computer Science, UAF, Sub Campus Burewala, Pakistan
Muhammad Asif Khan Department of Food Science and Technology,IUB, Bahawalpur, Pakistan
Hafiz Muhammad Haroon Department of Computer Science, UAF, Sub Campus Burewala, Pakistan
Sidra Habib Department of Computer Science, UAF, Sub Campus Burewala, Pakistan
Komal Nida Khan Department of Math and Statistics, University of Agriculture, Faisalabad Sub Campus Burewala, Pakistan
Saba Nasir Department of Management Science, University of Agriculture, Faisalabad Sub Campus Burewala, Pakistan
Sadaf Shakoor Department of Food Science, University of Agriculture, Faisalabad Sub Campus Burewala, Pakistan
Abstract
Insect pest have great influence in vegetable crop growth. It can be minimized by prediction of insect pest using environmental parameters, IOT and machine learning algorithms. The directly sensed environment conditions are used as input to the machine learning model to make binary decisions regarding the pest population according to the prevailing environmental conditions. After implementation in field 89.2% accuracy have achieved by using naïve bayes binary algorithm. The f1 recall, precision and support evaluation metrices have been used for algorithm evaluation. It is highly recommended for formers to increase the yield of vegetable crop.
Publication Details
Page(s) 31-31
DOI DOI not available
Published Journal: Abstract Book on International Conference on Life Sciences (ICLS-23) 11-12 May 22-23, Volume: 0, Issue: 0, Year: 2023

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Keywords
Naïve Bayes, Internet Of Things (Iot), smart farming, Insect Pest Prediction
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