Abstract
In the field of agriculture, crops are susceptible to attacks by pests. Accurately identifying and classifying crop pests, especially in their early stages of growth, is a significant challenge. Convolutional neural networks have become effective instruments for agricultural pest classification because of their capacity to extract and learn intricate information from photos. This study explores the use of transfer learning methods to compare efficient models with complex models to improve the effectiveness of pest and disease classification. The purpose of this study is to compare efficient models with complicated models using transfer learning techniques in order to increase the accuracy of pest and disease classification. By conducting experiments on the same pest and disease dataset, this research compares their performance in terms of accuracy, model size, computational resource consumption, and practical feasibility in real-world applications. The research results clearly demonstrate the performance comparison between different models, with efficient models excelling in some aspects while large models have advantages in others. This study contributes to better understanding for decision-makers in the agricultural sector on how to choose the appropriate deep learning models to enhance crop protection and increase yields.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.