1. AMANA ASIF LODHI - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
2. RAFAQAT KAZMI - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
3. SUNNIA IKRAM - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
4. AMNA IKRAM - Department of Computer Science & IT, Government Sadiq College Women University, Bahawalpur,
Pakistan.
5. AQSA - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
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.
Internet of things (IoT), Insect Pest Prediction, Naïve Bayes, Smart Farming.