Neuro-Cognitive Inspired Deep Learning for Enhanced Navigation in No-Till Agriculture Using Multisensory Data

No-till farming, an approach to combating climate change, poses obstacles to autonomous agricultural navigation. This approach often leads to uneven fields and varying crop densities, making navigation challenging and sometimes reducing crop yields compared to conventional tillage. However, when no-till practices are combined with conservation methods such as residue retention and crop rotation, it can mitigate these drawbacks and even improve productivity, especially in dry climates. These complex conditions require advanced autonomous mobile robots capable of effectively navigating uneven terrains, adjusting to fluctuating crop densities, and quickly responding to unexpected obstacles such as fallen branches. Drawing inspiration from neurocognitive science, this study introduces a novel deep learning methodology that focuses on immediate adaptability through sensory-steering coordination and multimodal sensory integration. By leveraging convolutional neural networks and a fusion mechanism for navigational commands, the proposed model skillfully navigates the dynamic and unstructured conditions of no-till agriculture, showcasing dependable capabilities in simulated 3D environments.