Design and construction of a smart variable rate sprayer system for weed-plant identification using image processing (case study: sugar beet farm)

This research highlights the potential of computer vision and machine learning algorithms to enhance weed control systems and decrease herbicide use in agriculture. The development of an efficient and cost-effective smart sprayer system has the capacity to not only benefit farmers financially, but also mitigate the environmental impact of herbicide application. Further investigation could explore the feasibility and practicality of implementing such systems on a larger scale in diverse crop fields. The recognition of weeds and crops based on their appearance characteristics is crucial for effective weed control systems, and the proposed BOVW algorithm demonstrated a high level of accuracy, reliability, and sensitivity in distinguishing between common weed species and sugar beet crops. The smart sprayer system, incorporating the BOVW algorithm, exhibited a high level of precision in identifying the product from weed species online. Notably, the provided sprayer system significantly reduced herbicide consumption by 78.93% and 69.38% in the best and worst mode of spraying, respectively. The findings suggest that the variable rate mode utilizing the BOVW detection algorithm represents the optimal mode of operation for the smart sprayer system.