Study on Machine Learning Algorithms for Reducing Pesticide Spray on Crops

Authors

  • Padma Nilesh Mishra, Shirshendu Maitra

Keywords:

machine learning algorithm,Machine learning algorithms,Reducing pesticide respray,Crops

Abstract

Machine learning can be used to optimize the application of pesticides in agriculture by predicting the amount of pesticide needed based on various factors such as crop type, growth stage, weather conditions, and soil composition. This can help reduce the amount of pesticide used, increase crop yield, and minimize environmental impact.Machine learning algorithms like logistic regression classification, polynomial regression, and K-nearest neighbor (KNN) can be used to classify which sections of a crop field require repeated pesticide spraying.These algorithms can analyze data on factors such as weather conditions, soil type, pest populations, and previous pesticide applications to determine which areas of the field are most susceptible to pest damage. By using this information, farmers can target their pesticide applications to only the areas that need it, instead of spraying the entire field repeatedly. Thus, in the paper, we discussed accuracy percentages based on different machine learning algorithms.

Published

2023-05-23

How to Cite

Padma Nilesh Mishra, Shirshendu Maitra. (2023). Study on Machine Learning Algorithms for Reducing Pesticide Spray on Crops. SJIS-P, 35(2), 107–113. Retrieved from http://sjis.scandinavian-iris.org/index.php/sjis/article/view/590

Issue

Section

Articles