# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 1 Introduction  
The maize varieties in China include mostly the DEKALB, Xianyu-335, and KX-7349 series, which can grow up to 3 m in height without chemical control. The late growth stage of maize is a pest-prone period; such pests consist mainly of the aphid, corn borer, armyworm, and cotton bollworm. Leaf spot, bacterial wilt, rust, leaf spot diseases, and other diseases also occur frequently. The agricultural equipment has a significant impact in crop production. In large areas of maize fields, new machines are required for operability and efficiency of the control of weeds, pathogens, and insects. Researchers started to study on agricultural machines a few decades ago. Sprinkling pesticides, planting, weeding, crop harvesting, and pest monitoring operations are conducted based on appropriate agricultural equipment [1; 2; 3; 4; 5]. In the agricultural field, however, the use of robots still accounts for only a small percentage of the total work [6]. For crops that grow in rows, such as maize, many machines are available to perform operations, such as the protection of plants between rows [7]. However, human drivers or operators are still needed for the machines to move between rows, particularly in the middle and late growth stages of maize. There are two main obstacles which hinder the development of agricultural mechanization in China: the feasibility of the work and the environmental adaptability. Agricultural robotic system can be divided into three main parts, depending on the function of each module: mobile platform, execution system, and operator control system. In field operations, most attention was paid to large agricultural tractors, given their widespread usage. In many of these studies, commercial tractors or farm machinery is modified to achieve autonomous operations [8; 9]. However, in the late growth stage of maize, it is difficult for large machinery to maneuver in the field due to space constraints. This study describes a novel unmanned vehicle that accomplishes driving within crop rows in maize fields. This vehicle uses machine vision and path-planning methods and is able to operate in lanes with a width greater than 60 cm. A number of autonomous driving agricultural machineries have been investigated [10; 11; 12]. Many research institutions have designed and produced autonomous vehicles and robots in the field management system to reduce labor and improve labor efficiency. Most autonomous navigation systems enable robots to autonomously navigate in fields based on real-time kinematic GPS [13]. Examples include the autonomous tractor equipped with a perception and actuation system for autonomous crop protection [9], an autonomous platform for robotic weeding [14], and an accurate GPS sensor that provides position information with errors as low as 0.01 m in the horizontal direction [15]. Most of the robots for field operation move along crop rows [16; 17; 18]. Such robots usually use a camera to identify crop rows that comprise the boundary of the travel area. Most research uses the color difference between crops and soil [19; 20; 21]. Some of the researchers improved object recognition by using a combination of the local power spectrum (LPS) of the lightness of the color and the full color gradient in both learning and inference algorithms [22; 23]. The object of such research includes sugar beet, rice, and other crops with low height [4]. The camera for image acquisition needs to be installed over the top of the crop. However, these techniques are difficult to apply in maize or sorghum, especially in the middle and late growth stages; the growth of stalks is very high and overlapping leaves tend to block the soil. Various devices, such as aircraft or inter-row machines, can be used to solve the problems related to large agricultural machinery on farmland [24; 25; 26]. A monocular vision navigation methodology for autonomous orchard vehicles is being used [27].