The proposed navigation algorithms can be extended to handle more complex agricultural tasks by integrating additional modules for plant-specific manipulation or harvesting. For plant-specific manipulation, the algorithms can be enhanced to identify and interact with individual plants based on their characteristics, such as size, shape, or health status. This could involve incorporating advanced computer vision techniques to detect specific plant features and develop strategies for targeted interventions, such as pruning, fertilizing, or pest control.
For harvesting tasks, the algorithms can be adapted to recognize ripe fruits or crops and perform precise harvesting actions. This may involve integrating robotic arms or end-effectors to physically interact with the plants and collect the produce. Machine learning models can be trained to differentiate between ripe and unripe fruits based on color, size, or other visual cues, enabling the robot to selectively harvest the mature crops while leaving the rest to ripen further.
By incorporating these functionalities into the existing navigation algorithms, the robotic platform can evolve into a versatile agricultural assistant capable of performing a wide range of tasks beyond simple navigation through crop rows.