Row-sensing Templates: A Generic 3D Sensor-based Approach to Robot Localization with Respect to Orchard Row Centerlines

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 work, we introduce a novel localization method that doesn't 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 re-building 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 degrees. Localization was robust, as errors didn't 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

Exploration on Teaching Reform of Comprehensive Chemistry Experiment in Normal Universities

Comprehensive chemistry experiment is a compulsory course for college chemistry majors, which is in a connecting position in the curriculum system. In view of the shortcomings in the traditional comprehensive chemistry experiment teaching, this paper discusses the reform from the aspects of the selection of teaching content and the evaluation method of the innovative experiment course of teaching methods. On this basis, the students' comprehensive chemistry experiment literacy, innovation consciousness and comprehensive experiment ability are comprehensively cultivated. Cultivating normal college students with both applied ability and practical teaching ability. Keywords: comprehensive chemistry experiment, innovation consciousness, experiment ability DOI: 10.7176/JEP/14-3-04 Publication date: January 31st 202

Exploration of Teaching Reform in Environmental Protection Equipment and Engineering Design Course under the Background of New Engineering

Environmental protection equipment and engineering design is an important foundational course for environmental majors in higher education institutions, which combines systematic theory with strong practicality. The article is based on research on learning situations and pain points in educational reform. Through a series of educational reform measures, a learning community is established, scientific research is strengthened to support teaching, teaching content is optimized, teaching cases are enriched, and diversified teaching practices are carried out; Expand the second classroom, build a teaching platform, establish a mentorship system for undergraduate students, and organize all students to participate in innovation and entrepreneurship competitions; Integrating ideological and political education into the curriculum, cultivating students' scientific thinking, and solving practical environmental problems. Since the implementation of this innovative model, it has broadened students' horizons and improved the quality of teaching; We have established a comprehensive and full-time education model, enhancing students' practical and innovative abilities; It cultivates students' scientific thinking and exercises their ability to solve practical environmental problems, which has certain promotion and reference significance for comprehensively promoting the reform of the environmental chemistry curriculum system. Keywords: New engineering, Environmental protection equipment and engineering design, Scientific thinking DOI: 10.7176/JEP/15-11-03 Publication date: October 30th 2024

Optimization-Based Motion Planning for Autonomous Agricultural Vehicles Turning in Constrained Headlands

Headland maneuvering is a crucial aspect of unmanned field operations for autonomous agricultural vehicles (AAVs). While motion planning for headland turning in open fields has been extensively studied and integrated into commercial auto-guidance systems, the existing methods primarily address scenarios with ample headland space and thus may not work in more constrained headland geometries. Commercial orchards often contain narrow and irregularly shaped headlands, which may include static obstacles,rendering the task of planning a smooth and collision-free turning trajectory difficult. To address this challenge, we propose an optimization-based motion planning algorithm for headland turning under geometrical constraints imposed by field geometry and obstacles

Revealing the chemical role of Al promoter with extremely low content of 0.85% in Fe2O3 for the High-Temperature Water-Gas Shift Reaction

The high-temperature water-gas shift (HT-WGS) reaction is critically important for the development of H2 production and Al is conventionally considered as a textural promoter to stabilize the surface of the iron oxide phase towards sintering. Here in this paper, we found that Al can also be a chemical promoter with an extremely low content of 0.85% in Fe2O3. The results show that due to the addition of Al (from 0.34 to 4.42%), the spinel structured FeAl2O4 formed, which can grasp Fe2+ and thus improved the CO conversion by redox mechanism. Lower content of Al with the amount of 0.85% (Fe1.95Al0.05O3) exhibited the best activity in terms of CO conversion for HT-WGS at 400 °C and 450 °C. Scanning transmission electron microscopy (STEM) confirmed that atomic isolation of Al atoms within Fe3O4 lattice not only optimizes the hydrogen-binding energy but also decreases the free energy of water formation, thus leading to excellent thermocatalytic activity of Al1-Fe2O3 catalyst. The results show that Al can be a chemical promoter and via engineering Al at atomic level, which is highly effective for rational design of HT-WGS catalysts with high performance. Keywords: Water-gas shift reaction, Al promoter, Chemistry role DOI: 10.7176/CMR/17-1-05 Publication date: March 30th 202

End-to-end deep learning for directly estimating grape yield from ground-based imagery

Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Continuous data collection using a vehicle-mounted sensing kit combined with collection of ground truth yield data at harvest using a commercial yield monitor allowed for the generation of a large dataset of 23,581 yield points and 107,933 images. Moreover, this study was conducted in a mechanically managed commercial vineyard, representing a challenging environment for image analysis but a common set of conditions in the California Central Valley. Three model architectures were tested: object detection, CNN regression, and transformer models. The object detection model was trained on hand-labeled images to localize grape bunches, and either bunch count or pixel area was summed to correlate with grape yield. Conversely, regression models were trained end-to-end to predict grape yield from image data without the need for hand labeling. Results demonstrated that both a transformer as well as the object detection model with pixel area processing performed comparably, with a mean absolute percent error of 18% and 18.5%, respectively on a representative holdout dataset. Saliency mapping was used to demonstrate the attention of the CNN model was localized near the predicted location of grape bunches, as well as on the top of the grapevine canopy. Overall, the study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale. Additionally, the end-to-end modeling approach was able to perform comparably to the object detection approach while eliminating the need for hand-labeling

Construction of Practical Teaching System of Environmental Engineering Specialty Under the Background of New Engineering

New engineering is the inevitable direction of higher engineering education reform in China. It is also a major choice for the development of environmental engineering specialty under the background of the new industrial revolution and new economic development. The practical teaching of environmental engineering is an important way to improve undergraduate experimental practical skills and cultivate scientific research interest, which is significant for the cultivating of high-quality applied talents. Under the background of new engineering, there are still some problems in the practical teaching of environmental engineering specialty in China, which need to be solved urgently. This paper first expounds on the problems existing in the practical teaching of environmental engineering and then puts forward the effective measures to construct the practical teaching innovative system of environmental engineering, to improve the effectiveness of practical teaching and the quality of applied talents. Keywords: new engineering; environmental engineering specialty; practical teaching system; innovation DOI: 10.7176/JEP/13-14-03 Publication date:May 31st 2022