• Virender Kumar

The Indian economy heavily depends on agriculture, crucial for addressing food shortages and ensuring nutritious food supply. However, farmers currently encounter challenges in identifying crop-damaging pests and selecting appropriate pesticides, often leading to the use of ineffective chemicals that reduce yields and cause financial losses. Traditional pest identification relies on skilled taxonomists assessing physical characteristics, posing limitations. To address this issue, experiments utilized shape features and machine learning methods such as support vector machines (SVM), k-nearest neighbors (KNN), naive Bayes (NB), and convolutional neural networks (CNN) to classify 15 insect classes in the IP102 dataset.

KNN, SVM, CNN, NB

"Insects Classification in Agriculture Field using Deep learning Technologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.i775-i783, June-2024, Available :http://www.jetir.org/papers/JETIR2406887.pdf

"Insects Classification in Agriculture Field using Deep learning Technologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppi775-i783, June-2024, Available at : http://www.jetir.org/papers/JETIR2406887.pdf