An Approach For Pest Detection and Classification Using Transfer Learning Algorithms

Agriculture is the foundation of many civilizations in addition to being the breath of survival among many living things. One of the most crucial tasks on this farming is pest control. The yield of agricultural crops is significantly impacted by insect infestations. Accurate and quick identification of parasitic insects in the earlier phases owing to deep learning algorithms advancements may save both micro- and macro significant disruption. The proposal's objective is to identify insect pests that have an impact on crop output in agriculture. This paper presents a brief assessment of the current approaches to pest identification and categorization using deep learning and machine learning models. Furthermore, it suggests a structure for the identification and classification of pests by means of the transfer learning models Pla-Net and GoogleNet. Many cattle ranchers are now earning a sizable income owing to the automation of a virus prediction model. This can improve the nation's food production by using pre-trained models and deep learning approaches. The system is evaluated using a sample dataset, and the outcomes are tested to demonstrate the effectiveness of the suggested strategy. Therefore as adopt to this work, this intends to create an efficient automated early pest detection and classification system that can help farmers more effectively and revolutionize the way farming is practiced.