Precision Agriculture: ML and DL-Based Detection and Classification of Agricultural Pests
Ashphak P. Khan1, Prerana M. Jangid2, Rajshri S. Patel3, Kirti R. Girase4, Namrata M. Patel5
1Ashphak P. Khan, Department of Computer Engineering, P.S.G.V.P. M’s D. N. Patel COE Shahada, Khetiya (Madhya Pradesh), India.
2Prerana M. Jangid, Department of Computer Engineering, P.S.G.V.P. M’s D. N. Patel COE Shahada, Shahada (Maharashtra), India.
3Rajshri S. Patel, Department of Computer Engineering, P.S.G.V.P. M’s D. N. Patel COE Shahada, Shahada (Maharashtra), India.
4Kirti R. Girase, Department of Computer Engineering, P.S.G.V.P. M’s D. N. Patel COE Shahada, Shahada (Maharashtra), India.
5Namrata M. Patel, Department of Computer Engineering, P.S.G.V.P. M’s D. N. Patel COE Shahada, Shahada (Maharashtra), India.
Manuscript received on 28 April 2025 | First Revised Manuscript received on 14 May 2025 | Second Revised Manuscript received on 16 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 1-6 | Volume-5 Issue-2 November 2025 | Retrieval Number: 100.1/ijae.A153505010525 | DOI: 10.54105/ijae.A1535.05021125
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Precision agriculture has become a vital strategy in modern farming, leveraging advanced technologies to enhance crop productivity and sustainability. One critical aspect of precision agriculture is the timely and accurate detection and classification of agricultural pests, which significantly impact crop health and yield. This study examines the application of machine learning (ML) and deep learning (DL) techniques, particularly convolutional neural networks (CNNs), for detecting and classifying agricultural pests. This research presents a comprehensive approach that utilizes CNN-based models to identify and categorize various pest species from images captured of farm fields. The methodology involves collecting and annotating a diverse dataset comprising images of multiple pest species and non-pest objects to ensure robust model training and validation. The CNN architecture is designed to extract intricate features from the images, enabling the model to differentiate between pest and non-pest instances effectively.
Keywords: Automated Pest Identification, Precision Agriculture, CNN, Early Detection.
Scope of the Article: Agriculture Engineering