Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2023, Vol 2, Issue 4
Abstract
In the realm of agriculture, crop yields of fundamental cereals such as rice, wheat, maize, soybeans, and sugarcane are adversely impacted by insect pest invasions, leading to significant reductions in agricultural output. Traditional manual identification of these pests is labor-intensive and time-consuming, underscoring the necessity for an automated early detection and classification system. Recent advancements in machine learning, particularly deep learning, have provided robust methodologies for the classification and detection of a diverse array of insect infestations in crop fields. However, inaccuracies in pest classification could inadvertently precipitate the use of inappropriate pesticides, further endangering both agricultural yields and the surrounding ecosystems. In light of this, the efficacy of nine distinct pre-trained deep learning algorithms was evaluated to discern their capability in the accurate detection and classification of insect pests. This assessment utilized two prevalent datasets, comprising ten pest classes of varied sizes. Among the transfer learning techniques scrutinized, adaptations of ResNet-50 and ResNet-101 were deployed. It was observed that ResNet-50, when employed in a transfer learning paradigm, achieved an exemplary classification accuracy of 99.40% in the detection of agricultural pests. Such a high level of precision represents a significant advancement in the field of precision agriculture.
Authors and Affiliations
Christine Dewi,Henoch Juli Christanto,Guowei Dai
Keywords
Related Articles
- EP ID EP731895
- DOI https://doi.org/10.56578/ataiml020402
- Views 42
- Downloads 1
Analysis of Artificial Intelligence and Natural Language Processing Significance as Expert Systems Support for E-Health Using Pre-Train Deep Learning Models
Artificial intelligence (AI) and natural language processing (NLP) are relentless technologies for healthcare that can support a strong and secure digital system with embedded applications of internet of things (IoTs). T...
A Survey on Multimedia Ontologies for a Semantic Annotation of Cinematographic Resources for the Web of Data
The Semantic Web provides approaches and tools that allow for the processing and analysis of online content, including multimedia resources. Multimedia resources like videos, audios, and photos are increasingly common in...
Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine
This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid...
A Comprehensive Review of Ant Colony Optimization in Swarm Intelligence for Complex Problem Solving
Swarm intelligence (SI) has emerged as a transformative approach in solving complex optimization problems by drawing inspiration from collective behaviors observed in nature, particularly among social animals and insects...
DNA-Level Enhanced Vigenère Encryption for Securing Color Images
This study presents the development of a novel method for color image encryption, leveraging an enhanced Vigenère algorithm. The conventional Vigenère cipher is augmented with substantial substitution tables derived from...