Real Time Identification of Harmful Birds in Agriculture Field using Computer Vision

Agriculture is essential to the global economy and food security. Insect and bird attacks have now been found in recent research that could cause crop loss. Crops are prone to bird attacks throughout the early stages of crop production and are particularly sensitive from the milky stages up to maturation. Observing such birds, which are harmful to agriculture, is an expensive, time-consuming, and possibly error-prone operation. Furthermore, a vast area necessitates a huge labour force for bird scaring. The concept of automated monitoring through computer vision holds significant appeal. This is because it has the potential to facilitate the collection of comprehensive data on a much larger scale than is currently feasible. In this work, we leverage effective characteristics from images of 10 different hazardous birds, Especially in the realm of identification we employing transfer-learned deep learning models which holds promise. The resemblance of various bird features, as well as the difficulty of distinguishing them at a glimpse, has become a significant challenge. We used more than 1000 images of harmful birds from various species for model training. In experiments on two different transfer learned deep CNN models, the xceptionNet model showed the best performance with a test accuracy of 94% when compared to the relatively low memory model VGG19, which had a test accuracy of 80%.The analysis also revealed that the xception model performed better in terms of accuracy, precision, recall, and F1-score.