Identification of weeds in cotton fields at various growth stages using color feature techniques

Weeds affect the growth and yield of cotton ( Gossypium hirsutum ), which is the most prevalent commercially viable non-food crop. Accurate differentiation between weeds and cotton plants is crucial for implementing precise weed-control strategies, optimizing crop yields, and minimizing herbicide usage. In this study, we aimed to investigate the effectiveness of using color feature techniques combined with Otsu's method (OTSU) to distinguish weeds from cotton plants at various growth stages. The results revealed that the image segmentation approach achieved a recognition rate of 74.1 % during the second growth stage. In the third growth stage, the weed identification process primarily targeted lambsquarters ( Chenopodium album ). The standard deviation (SD) difference between the red (R), green (G), and blue (B) components of the plant images revealed that the difference between the SD values of R and B was <5, which could serve as an effective threshold for identifying lambsquarters. Additionally, the recognition rates for cotton and lambsquarters were 71.4 % and 92.9 %, respectively, and the overall recognition rate reached 82.1 % during the identification process. The results of this study have practical implications for weed management practices, such as targeted herbicide application, mitigation of environmental impacts, and contribution to higher crop yields. Future research will focus on refining these methods by exploring advanced image processing techniques that integrate deep learning for automatic feature extraction, which will be evaluated for handling complex agronomic scenarios, such as leaf occlusion, varying growth stages, and image noise. • Developed novel color feature techniques for precise weed identification in cotton fields. • Utilized the OTSU method for effective image processing in weed detection. • Developed a method using dark red stem features and achieved an overall 74.1 % cotton recognition rate. • Identify lambsquarters herb with 92.9 % accuracy in 3rd stage seedlings using R & B color deviation. • Enhanced crop management with innovative weed-cotton differentiation strategies.