Pest Identification and Control using Deep Learning and Augmented Reality

Abstract

It is crucial to comprehend how insect pest populations affect the subsequent yield or harvest since the ultimate goal of agriculture is to provide a sustained economic production of crop products. Using pesticides is the simplest technique to manage the pest infestation. However, using pesticides improperly or in excess can harm both people and animals as well as the plants. Machine learning algorithms and image processing techniques are widely used in agricultural research, and they have significant potential, particularly in plant protection, which ultimately leads to crop management. This paper highlights the detection of pests and their control measures. A smartphone camera will capture photographs of the pests through a mobile app built using the Flutter framework. The images are then analyzed in the app using various transfer learning based models for available pest identification kaggle dataset. The flutter framework offers the ability to monitor targets in real-time on a mobile device and aids in visualizing the detected pest by integrating augmented reality on to the app.