Image Processing-Based Solution to Repel Crop-Damaging Wild Animals

Abstract

Two-thirds of Sri Lanka’s population is directly dependent on agriculture, which generates one-third of the nation’s GDP. However, crop efficiency in Sri Lanka has declined over the years due to several issues including sub-farm maintenance, destruction caused by wild animals, and unethical farming practices. Among them, the destruction caused by wild animals has led to conflicts between animals and humans causing loss of both animals and human lives in the past. There are a number of technical solutions proposed to solve the above problem, especially in the form of animal repellants. However, such solutions have several limitations, such as the small number of animal groups to be identified and the short distances they can be detected, and the lack of understanding of harmful animal populations. This research proposes an animal-repellent methodology considering several features of animals such as colors, coats, shape, and noise made by animals both in daytime and nighttime. The number of animals approaching crops is also detected and the behavior of animals is monitored to avoid false alarms. The research uses a wide range of techniques such as image processing and deep learning for the above purpose on audio, visual, and image data sets collected from the mentioned animal groups. The solution demonstrated a 90% accuracy for animal identification during the day, and 84% accuracy for animal 2 W. P. S. Fernando et al. identification at night, whereas the accuracy of studying animal behavior patterns is 90% and animal sounds were identified with 87% accuracy