Cost-efficient algorithm for autonomous cultivators: Implementing template matching with field digital twins for precision agriculture

The paper focuses on the development of a vision system to automate the position control of a cultivator used for crop weeding. The vision algorithm allows monitoring of the cultivator's misalignment with respect to crop rows, with real-time processing. The key content includes the introduction of a self-generated digital twin of the field model for numerical validation of different computer vision solutions and a comparison of three vision algorithms for measuring deviation. The objectives of the study are to improve the precision of misalignment measurements and ensure safe and accurate movement of the cultivator. The rationale behind the study is to address constraints such as camera installation and crop color, and to emphasize the importance of a confidence estimation feature for accurate measurement. The paper also provides an overview of related works in the literature, highlighting the two phases of plant identification and deviation measurement. Tests carried out on soybean and maize crops demonstrate the improvements allowed by the proposed algorithm in terms of higher measurement precision, even in the presence of high weed infestation or a significant number of missing plants. Additionally, the paper suggests analysis simplifications to enhance the algorithm's speed while maintaining satisfactory measurement accuracy.