Time-efficient low-resolution RGB aerial imaging for precision mapping of weed types in site-specific herbicide application

• Published in: Crop protection 2024-10, Vol.184, p.106805, Article 106805
• Main Authors: Panduangnat, Lalita, Posom, Jetsada, Saikaew, Kanda, Phuphaphud, Arthit, Wongpichet, Seree, Chinapas, Adulwit, Sukpancharoen, Somboon, Saengprachatanarug, Khwantri
• Format: Article
• Language: English
• Subjects: Images processing; Spraying drone; Sugarcane; UAV; Weed detection; Weed mapping
• ISSN: 0261-2194
• DOI: 10.1016/j.cropro.2024.106805

An efficient method for weed detection and the precise generation of spraying maps is crucial to optimizing weed management strategies and minimizing the costs associated with herbicide use. This study presents a method that leverages low-resolution UAV images to accurately detect and map weeds in sugarcane fields. The proposed approach integrates RGB images, vegetation indices and the HSV colour space, enhancing segmentation through histogram equalization (HE) and object-based image analysis (OBIA). The models developed in this study demonstrate exceptional performance in weed detection, with the most suitable dataset, achieving a detection capability of 95.78% for Broad-leaved Weeds (BLW) and the highest accuracy of 94.45% for Narrow-leaved Weeds (NLW). When considering multiple targets such as BLW, NLW, soil and sugarcane, the models exhibit a detection accuracy of 89.69%. Furthermore, the precision spraying maps generated by the coverage model method (CM) demonstrate remarkable accuracy, reaching 97.50% for weed control using agricultural drones. This method offers an efficient, cost-effective, and timely solution for precise weed detection, leading to improved weed control outcomes by enabling the selection of appropriate chemical substances tailored to each weed species. It reduces repetitive spraying costs and minimises chemical usage through spot spraying. •Developed a method to use low-res UAV imaging for precise weed mapping in sugarcane fields.•Applied histogram equalization to enhance OBIA's accuracy in identifying similar-looking weeds.•Optimized weed type detection using RGB, vegetation indices, and HSV in a machine learning.•Achieved 97.50% accuracy in creating weed spraying maps.•The proposed method offers cost-effectiveness, precision, and rapid implementation.