Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV’s reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system.

Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, and harvesting. Row crop navigation presents unique challenges, and mapping plays a crucial role in optimizing routes and avoiding obstacles in coverage path planning (CPP), which is essential for efficient agricultural operations. This study proposes a simple method for using Unmanned Aerial Vehicles (UAVs) to create maps and its application to row crop navigation. A case study is presented to demonstrate the method’s viability and illustrate how the resulting map can be applied in agricultural scenarios. This study focused on two major row crops, namely corn and soybean, but the results indicate that map creation is feasible when the inter-row spaces are not obscured by canopy cover from the adjacent rows. Although the study did not apply the map in a real-world scenario, it offers valuable insights for guiding future research.

Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, or harvesting. Row crop navigation is a challenging task, and mapping can help to optimize routes and avoid obstacles in coverage path planning (CPP), which is vital in agricultural operations. This study proposed a simple method to use Unmanned Aerial Vehicles (UAVs) to create maps for row crop navigation. It also shows a case study to show the method’s viability and how the map can be used in agricultural scenarios. The results from this study show that map creation is possible when inter-row spaces are visible and not covered by the canopy created by plants from adjacent rows.

Increasing food production is essential to meet the demands of the growing global population. Enhanced food production ensures food security, reduces hunger, and supports economic stability. More food production requires optimization of land use, conservation of resources, and minimizing environmental impact by adopting innovative agricultural practices and technologies, such as automated farming practices and highyield crop varieties. Agricultural robots are an integral component of automated farming operations. Flex sensors are simple tactile sensors that can be used to enhance the autonomous navigation capabilities of agricultural robots. These sensors, which detect changes in resistance caused by bending or flexing, are a good option for tactile sensing of robotic movements. By incorporating flex sensors, agricultural robots can more accurately navigate complex and dynamic farm environments, adjusting their operations based on real-time feedback. This paper shows a novel way of using flex sensors to assess whether plant materials like stems, leaves, and branches are passable or non-passable by robots. Flex sensors can also contribute to developing adaptable and responsive robotic systems requiring tactile sensing information in agricultural scenarios, increasing the efficiency and productivity of the farm production systems. Integrating flex sensors in agricultural robots can pave the way for simple tactile sensing for different tasks for automated farm operations.