• Mr. Gorikapudi Maneedher babu
• Daram Sowmya
• Nallapati Venkata Bhargavi
• Penubothu Tejasri
• Allu Bhavni

Accurate and timely identification of insect pests is crucial for effective crop management. Traditional methods often rely on manual inspection, which is time consuming, labor-intensive, and prone to human error. This paper explores the application of modern machine learning techniques to automate the process of insect classification and detection in field crops. By leveraging deep learning architectures, such as convolutional neural networks (CNNs), we aim to develop a robust and efficient system that can accurately identify different insect species from images captured in field environments. Our approach involves data collection, preprocessing, model training, and evaluation. Experimental results demonstrate the effectiveness of the proposed method in achieving high classification and detection accuracy, thereby providing valuable insights for farmers and agricultural experts in pest control and crop protection.

Insect classification, Insect detection, Machine learning, Convolutional neural networks (CNNs), Model training, Classification accuracy, Detection accuracy

"INSECT CLASSIFICATION AND DETECTION IN FIELD CROPSUSING CNN MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.339-346, November-2024, Available :http://www.jetir.org/papers/JETIRGO06034.pdf

"INSECT CLASSIFICATION AND DETECTION IN FIELD CROPSUSING CNN MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp339-346, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06034.pdf