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In this paper, we present an automatic motion planner for agricultural robots that allows us to set up a robot to follow rows without having to explicitly enter waypoints. In most cases, when robots are used to cover large agricultural areas, they will need waypoints as inputs, either as premeasured coordinates in an outdoor environment, or as positions in a map in an indoor environment. This can be a tedious process as several hundreds or even thousands of waypoints will be needed for large farms. In particular, we find that in unstructured environments such as the ones found on farms, the need for waypoints increases. In this paper, we present an approach that enables robots to safely traverse not only between straight rows but also through curved rows without the need for any predetermined waypoints. Most types of infrastructure found in agriculture, such as polytunnels, are built on uneven terrain, thus containing a mix of straight and curved plant rows, for which traditional methods of row following will fail. Different from traditional approaches of row following that only consider straight‐line‐of‐sight rows, our approach identifies the rows on each side with the goal of staying in the middle of the rows, even if the rows are not straight. Waypoints are only needed on the very extreme of the rows, and these will be automatically generated by the system. With our approach, the robot can just be placed in the corner of the field and will then determine the trajectory without further input from the user. We thus obtain an approach that can reduce the installation time from potentially hours to just a matter of minutes. The final autonomous system is low cost and efficient for various tasks that requires moving between plant rows inside a polytunnel. Several experiments were performed and the robot demonstrates 1.4% position drift over 21 m of navigation path. [ABSTRACT FROM AUTHOR]

The growing world population calls for more efficient and sustainable farming technologies. Automating agricultural tasks has great potential to improve farming technologies. A key requirement for full automation is the ability of agricultural vehicles to accurately navigate entire fields without damaging value crops. One important precondition for autonomous navigation is localization, that is, the ability of a vehicle to accurately estimate its pose relative to the crops. A majority of localization approaches detect crop rows to track the heading and lateral offset of the vehicle. This is sufficient to guide the vehicle along crop rows while driving inside the field. However, switching between rows requires a longitudinal pose estimate to determine when to turn at the end of the field. Additionally, at the end of the field sensor data contains less crop row structure and more noise from wild growing vegetation. This can lead to false‐positive crop row detections. In this paper, we present a localization approach that goes beyond state‐of‐the‐art crop row following algorithms by providing robust pose estimates not only inside the field but also at the end of the field. The underlying concept of our approach is to estimate the vehicle pose relative to a global navigation satellite system (GNSS)‐referenced map of crop rows. This allows us to fuse crop row detections with GNSS signals to obtain a pose estimate with the accuracy comparable to a row following approach in the heading and lateral offset, while at the same time maintaining at least GNSS accuracy along the row. Employing a GNSS‐referenced map of crop rows poses several challenges. To relate the detected crop rows to those in the map, we propose a data association strategy that finds correspondences between two sets of lines, that is, crop rows. Furthermore, we improve the GNSS‐based longitudinal pose estimate by detecting the end of the field from vegetation data. Additionally, we introduce a novel method to determine false‐positive crop row detections to increase the overall robustness in particular in challenging scenarios at the end of the field. Extensive real‐world experiments on three different types of crops demonstrate that our localization approach is well suited for fully autonomous navigation in entire fields. [ABSTRACT FROM AUTHOR]

Maize monoculture is one of the major restrictions limiting maize productivity in Western Ethiopia. Although the inclusion of legumes in cropping systems is an essential approach for the sustainable management of farming systems and for reducing the nitrogen (N) fertilizer requirement for maize production in the long term, the effects of soybean on the sustainability of maize productivity and soil fertility are unclear in Ethiopia. Continuous cropping of maize has led to extensive degradation of soil and a decrease in crop productivity in Western Ethiopia. Thus, the study was conducted to compare the long‐term impact of soybean on the sustainability of the production system in soybean–maize rotation and to monitor soil fertility dynamics in soybean–maize rotational systems. Nine different soybean–maize rotation treatments were laid out in Randomized Complete Block Design (RCBD) with three replications. The study results showed that soybean–maize rotation gave a relatively steady yield compared to the maize mono‐cropping system. Soybean–maize rotation improves the productivity of component crops in cropping systems. The highest maize grain yield was recorded from soybean–maize rotation with fertilizer application for the two components (RS + M+) and soybean–maize rotation without fertilizer application for the soybean component (RS‐M+), respectively. Soybean grain yield was significantly correlated with OC (%), OM (%), and TN (%), whereas maize yield was adversely correlated with soil parameters, except soil pH. Overall, the knowledge we can contribute to the readers from this study is that soybean–maize rotation plays an important role in achieving sustainable agriculture by increasing soil fertility and achieving stable soybean and maize yields and soybean should be inoculated with Rhizobium strain year after year continuously. Thus, it can be concluded that soybean–maize rotation with fertilizer applications (RS + M+) for two components can be used in maize belt areas of Western Ethiopia. [ABSTRACT FROM AUTHOR]

The automation of agricultural machines is an irreversible trend considering the demand for improved productivity and lack of labor in handling agricultural tasks. Unstructured working environments and weather often inhibit a seemingly simple task from being fully autonomously performed. In this context, we propose a remote driving system (RDS) to aid agricultural machines designed to operate autonomously. Particularly, we modify a commercial speed sprayer for orchard environments into a robotic speed sprayer to evaluate the proposed RDS's usability and test three sensor configurations in terms of human performance. Furthermore, we propose a confidence error ellipse‐based task performance measure to evaluate human performance. In addition, we present field experimental results describing how the sensor configurations affect human performance. We find that a combination of a semiautonomous line tracking device and a wide‐angle camera is the most effective for spraying. Finally, we discuss how to improve the proposed RDS in terms of usability and obtain a more accurate measure of human performance. [ABSTRACT FROM AUTHOR]

Accurate robot localization relative to orchard row centerlines is essential for autonomous guidance where satellite signals are often obstructed by foliage. Existing sensor‐based approaches rely on various features extracted from images and point clouds. However, any selected features are not available consistently, because the visual and geometrical characteristics of orchard rows change drastically when tree types, growth stages, canopy management practices, seasons, and weather conditions change. In this study, we introduce a novel localization method that does not rely on features; instead, it relies on the concept of a row‐sensing template, which is the expected observation of a 3D sensor traveling in an orchard row, when the sensor is anywhere on the centerline and perfectly aligned with it. First, the template is built using a few measurements, provided that the sensor's true pose with respect to the centerline is available. Then, during navigation, the best pose estimate (and its confidence) is estimated by maximizing the match between the template and the sensed point cloud using particle‐filtering. The method can adapt to various orchards and conditions by rebuilding the template. Experiments were performed in a vineyard, and in an orchard in different seasons. Results showed that the lateral mean absolute error (MAE) was less than 3.6% of the row width, and the heading MAE was less than 1.72°. Localization was robust, as errors did not increase when less than 75% of measurement points were missing. The results indicate that template‐based localization can provide a generic approach for accurate and robust localization in real‐world orchards. [ABSTRACT FROM AUTHOR]

Pesticide spraying is one of the risky and very essential task in agriculture to protect plants from harmful organisms. Many farmers are used manual pesticide spraying devices that they carry these devices on their shoulders and spray on the entire plant. Due to this, harmful pesticide chemicals spread into the air and may enter into the body of farmers and farm workers through the breathing system and mostly affect their body parts even, so many farmers lost their life. For this serious problem, we present the best solution to perform this task without the intervention of man is to utilize a robotic system in an agriculture. This paper analyses the state‐of‐the‐art reviews in the development of autonomous pesticide spraying robots that are divided into four main categories; platform mobility and steering, localization and navigation control, sensing and target detection, and pesticide spraying arrangement. This paper also focused on the efficient methodology that can be used for future perspectives to enhance pesticide spraying operations in an agriculture. In this paper, to achieve target spraying operation, a suitable navigation control algorithm for platform movement and an efficient trajectory planning algorithm for shortest link movement are designed which covers the shortest distance to visit all targeted pests on the plant. This technique will help to reduce the overdose of toxic pesticides hence minimize the wastage of pesticides and also control its dangerous effects on the human being and on the environment. [ABSTRACT FROM AUTHOR]

This paper describes a light detection and ranging (LiDAR)‐based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM‐30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row‐based and high‐leaf‐cover semistructured crop plantations, such as corn (Zea mays) and sorghum (Sorghum bicolor). The key contribution of our work is a LiDAR‐based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low‐hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within‐row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row‐lines based on current and previous information. The proposed LiDAR‐based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR‐based navigation in realistic field environments for small under‐canopy robots. [ABSTRACT FROM AUTHOR]

The scientific areas of plant genomics and phenomics are capable of improving plant productivity, yet they are limited by the manual labor that is currently required to perform in-field measurement, and a lack of technology for measuring the physical performance of crops growing in the field. A variety of sensor technology has the potential to efficiently measure plant characteristics that are related to production. Recent advances have also shown that autonomous airborne and manually driven ground-based sensor platforms provide practical mechanisms for deploying the sensors in the field. This paper advances the state-of-the-art by developing and rigorously testing an efficient system for high throughput in-field agricultural row-crop phenotyping. The system comprises an autonomous unmanned ground-vehicle robot for data acquisition and an efficient data post-processing framework to provide phenotype information over large-scale real-world plant-science trials. Experiments were performed at three trial locations at two different times of year, resulting in a total traversal of 43.8 km to scan 7.24 hectares and 2423 plots (including repeated scans). The height and canopy closure data were found to be highly repeatable ( r2 = 1.00 N = 280, r2 = 0.99 N = 280, respectively) and accurate with respect to manually gathered field data ( r2 = 0.95 N = 470, r2 = 0.91 N = 361, respectively), yet more objective and less-reliant on human skill and experience. The system was found to be a more labor-efficient mechanism for gathering data, which compares favorably to current standard manual practices. [ABSTRACT FROM AUTHOR]