Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer

Controlling weed infestation through chemicals (herbicides & pesticides) is essential for crop yield. However, excessive use of these chemicals has caused severe agronomic and environmental problems. According to accurate weed detection, an appropriate dose of herbicides is recommended in site-specific weed management (SSWM) applications, which may ultimately promote chemical saving while enhancing its effectiveness. In this context of SSWM, an accurate detection and recognition system needs to be established for recognizing weeds and crops to carry out precise agrochemical treatments in real-time applications. Unmanned aerial vehicles (UAVs) and other robots offer potential in precision agriculture (PA) applications by monitoring farmland on a per-plant basis, as they have the capability of acquiring high-resolution imagery providing detailed information for the distribution of crops and weeds in the field. In this regard, UAVs offer a cost-efficient solution for providing excellent survey capabilities. In this study, a deep learning system is developed for identifying weeds and crops in croplands. The developed system was implemented and evaluated using high-resolution UAV imagery captured over two different target fields: pea and strawberry; the developed system was able to identify weeds with an average accuracy of 95.3%, whereas the overall average accuracy (crops and weeds) was 94.73% for both the fields. The average kappa coefficient of the developed system was 0.89. The developed deep learning system outperformed the existing machine learning and deep learning-based approaches on comparison and can be embedded into a precision sprayer for adopting the SSWM strategy.