Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics

Precise scene understanding is key for most robot monitoring and intervention tasks in agriculture. In this work we present PAg-NeRF which is a novel NeRF-based system that enables 3D panoptic scene understanding. Our representation is trained using an image sequence with noisy robot odometry poses and automatic panoptic predictions with inconsistent IDs between frames. Despite this noisy input, our system is able to output scene geometry, photo-realistic renders and 3D consistent panoptic representations with consistent instance IDs. We evaluate this novel system in a very challenging horticultural scenario and in doing so demonstrate an end-to-end trainable system that can make use of noisy robot poses rather than precise poses that have to be pre-calculated. Compared to a baseline approach the peak signal to noise ratio is improved from 21.34dB to 23.37dB while the panoptic quality improves from 56.65% to 70.08%. Furthermore, our approach is faster and can be tuned to improve inference time by more than a factor of 2 while being memory efficient with approximately 12 times fewer parameters

Research status of agricultural robot technology

According to the different agricultural production uses, agricultural robots were classified, mainly including agricultural information collection robots, pruning robots, grafting robots, transplanting robots, spraying robots and picking robots. The research status of mainstream agricultural robots at home and abroad were introduced, and their working principles and characteristics were expounded. Finally, the problems existing in the key technologies of existing agricultural robots and their future development directions were put forward

Application progress of machine vision technology in the field of modern agricultural equipment

With the rapid progress of image processing algorithms and computer equipment, the development of machine vision technology in the field of modern agricultural equipment is in the ascendant, and major application results have been obtained in many production links to improve the efficiency and automation of agricultural production. In the face of China, the world's largest agricultural market, agricultural machine vision equipment undoubtedly has tremendous development potential and market prospects. This paper introduces the research and application of machine vision technology in agricultural equipment in the fields of agricultural product sorting, production automation, pest control, picking machinery and navigation and positioning, analyzes and summarizes the current problems, and looks forward to the future development trend

MOSYSS Project - Monitoring SYstem of Soils at multiScale. Monitoring system of physical, chemical and biological soil parameters in relation to forest and agricultural land management.

MOSYSS is a project launched in June 2010 by the Agriculture, Forestation and Fisheries Department of the Marche Region in Italy. It has been coordinated by the Regional Soil Observatory as part of the assessment activities of the Rural Development Plan (RDP) Marche 2007-2013 as laid down in the Common Monitoring and Assessment Framework. Among the objectives there is the creation of a permanent soil monitoring system for the whole Marche territory, combining technical and scientific requirements (e.g. rigor and representativeness) whilst optimizing financial and organizational resources. The information obtainable from the monitoring system could potentially be upscale, on a functional basis, in other existing soil and biodiversity monitoring networks at national and European level. The main function of the project is to investigate soils starting from their intrinsic properties ( e.g. chemical, physical or biological) to obtain a detailed evaluation of their current "quality" status, and to monitor, over time, changes in these parameters by repeating the monitoring campaign at pre-established time intervals.JRC.H.5-Land Resources Managemen

Agroenvironmental transformation in the Sahel: Another kind of “Green Revolution"

millions fed, food security, Sahel, Zai, Stone bunds, Agroforestry, Soil management,

Conserving tropical biodiversity via market forces and spatial targeting

The recent report from the Secretariat of the Convention on Biological Diversity [(2010) Global Biodiversity Outlook 3] acknowledges that ongoing biodiversity loss necessitates swift, radical action. Protecting undisturbed lands, although vital, is clearly insufficient, and the key role of unprotected, private land owned is being increasingly recognized. Seeking to avoid common assumptions of a social planner backed by government interventions, the present work focuses on the incentives of the individual landowner. We use detailed data to show that successful conservation on private land depends on three factors: conservation effectiveness (impact on target species), private costs (especially reductions in production), and private benefits (the extent to which conservation activities provide compensation, for example, by enhancing the value of remaining production). By examining the high-profile issue of palm-oil production in a major tropical biodiversity hotspot, we show that the levels of both conservation effectiveness and private costs are inherently spatial; varying the location of conservation activities can radically change both their effectiveness and private cost implications. We also use an economic choice experiment to show that consumers’ willingness to pay for conservation-grade palm-oil products has the potential to incentivize private producers sufficiently to engage in conservation activities, supporting vulnerable International Union for Conservation of Nature Red Listed species. However, these incentives vary according to the scale and efficiency of production and the extent to which conservation is targeted to optimize its cost-effectiveness. Our integrated, interdisciplinary approach shows how strategies to harness the power of the market can usefully complement existing—and to-date insufficient—approaches to conservation

Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study

The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases

EXPLORING THE SOCIO-ECOLOGICAL DIMENSIONS OF AGRICULTURAL EXPANSION IN NEW HAMPSHIRE

Forest ecosystems and agriculture represent coupled socio-ecological systems that are shaped by human activity. The extensification of agriculture (expansion of food production on the landscape) can cause significant changes in land use, and can contribute to the degradation of biodiverse ecosystems and the services these systems provide. Yet the need to increase food production capacity, either through agricultural intensification or extensification, continues to rise. In this dissertation, I address the critical issue of agricultural extensification from several angles. The first chapter assesses agricultural expansion through the lens of urban and peri-urban agriculture (UPA) through systematic review of the literature. I considered the availability of global data sets regarding UPA\u27s impact on ecosystem services and disservices, as well as its land sparing potential. This literature review showed that while there has been an increase in research exploring the intersection between UPA and ecosystem services, there is still a need to include the quantification of ecosystem services and functions to shed light on the ecological tradeoffs associated with agricultural production in the built environment. The second, third, and final chapters focus on a mixed-methods study aimed at exploring New Hampshire resident perception of agricultural expansion in the state. New Hampshire is experiencing a landscape shift back to agricultural production, as the numbers of farms and area in agricultural production are increasing. As a predominately forested state, increasing agricultural production in New Hampshire would require some forestland conversion, a change residents may not favor. I surveyed two populations in New Hampshire, self-identified food system stakeholders (e.g., farmers, public health professionals, and technical assistance providers) and a sample from the general population. Roughly 600 residents completed the survey, including 494 individuals from the statewide sample population, and 103 food system stakeholders. The survey included traditional written questions, as well as sets of images to understand how resident perception (visual preference) might influence potential future agricultural land use. Objectives of this study were to understand resident: (1) general perception of forestland conversion to agriculture, (2) measured level of acceptance of agricultural expansion on the landscape, (3) perception of ecosystem services from different types of farm landscapes, (4) willingness to live next to farms, and (5) consumer behavior related to locally grown food. Additionally, I sought to identify socio-economic factors that account for the differences between each population in terms of their landscape perception and preference. My findings suggest that there are differences in agricultural landscape preferences and perceptions between the general population and those who consider themselves food system stakeholders. While the response patterns were similar between each population, not surprisingly, food system stakeholders indicated that they were more accepting of agricultural expansion and more willing to live next to farms. In terms of landscape appeal, the statewide sample population rated forestland more appealing than cropland, while the food system stakeholders preferred cropland to forestland. My results show an interesting relationship between agricultural landscape preferences and consumer behavior. I found that overall consumer behavior favors local food purchasing, but while consumers may want to purchase locally grown food, they may not want to live next to the working farms that produce that food. Additionally, my findings suggest that household income and gender are the two most important socio-economic predictor variables related to agricultural landscape perception and preference, and consumer behavior of locally grown foods. The complexity of human attitudes and behaviors is a challenge for interest groups focused on increasing food production in the state. While my findings are just a snapshot in time, an improved understanding of how residents perceive agricultural expansion in the state, including forestland conversion, their willingness to live next to agricultural land, as well as their consumer behavior of locally grown foods could assist policymakers and land use planners in decision-making related to increasing agricultural production in the state