Deep Learning Based Classification System for Identifying Weeds Using High-Resolution UAV Imagery

In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this problem Omega, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. In this paper, we propose a new vision-based classification system for identifying weeds in vegetable fields such as spinach, beet and bean by applying convolutional neural networks (CNNs) and crop lines information. In this study, we combine deep learning with line detection to enforce the classification procedure. The proposed method is applied to high-resolution Unmanned Aerial Vehicles (UAV) images of vegetables taken about 20m above the soil. We have performed an extensive evaluation of the method with real data. The results showed that the proposed method of weeds detection was effective in different crop fields. The overall precision for the beet, spinach and bean is respectively of 93%, 81% and 69%.