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第 4 卷 第 3 期 2023 年 8 月 Vol 4 No 3 August 2023 智 能 化 农 业 装 备 学 报 中 英 文 Journal of Intelligent Agricultural Mechanization Development status and trends of intelligent control technology in unmanned farms QIAN Zhenjie JIN Chengqian LIU Zheng YANG Tengxiang Nanjing Institute of Agricultural Mechanisation Ministry of Agriculture and Rural Affairs Nanjing 210014 China Abstract With China s urbanization land circulation has emerged as an avenue for efficient and intensive land use Embracing technology as a primary driver the unmanned farm technology model represents a bold attempt at sustainable agriculture development This review focuses on the frontier and development trajectory of key core technologies and addresses the significant industrial challenges of insufficient accumulation of data on unmanned farms unknown interaction mechanisms among the environment plants and equipment and lack of multi parameter integration and regulation strategies for intelligent equipment Technologies such as autonomous positioning and navigation online professional sensor work obstacle ination perception path planning decision making multi machine collaboration autonomous operation and variable operation technology have been implemented in this study The research shows that in future realizing unmanned farms requires special sensors for agricultural intelligent equipment precise operational decision control systems and practically intelligent equipment At meanwhile addressing such challenges necessitates a focus on core technologies such as insufficient basic big data accumulation of unmanned farms modeling of environment plant equipment interaction mechanism and intelligent decision control algorithm This comprehensive improvement in automation intelligence and environmental sustainability of agricultural production marks the future development trajectory of unmanned farms Keywords unmanned farm intelligent control technology agriculture perception sensors intelligent task automation CLC number S24 Documents code A Article ID 2096 7217 2023 03 0001 13 QIAN Zhenjie JIN Chengqian LIU Zheng YANG Tengxiang Development status and trends of intelligent control technology in unmanned farms J Journal of Intelligent Agricultural Mechanization 2023 4 3 1 13 钱 震 杰 金 诚 谦 刘 政 杨 腾 祥 无 人 农 场 中 的 智 能 控 制 技 术 应 用 现 状 与 趋 势 J 智 能 化 农 业 装 备 学 报 中 英 文 2023 4 3 1 13 0 Introduction Sustaining the global population requires long term planning integrated into future agricultural policies The agriculture industry confronts challenges such as food self sufficiency and rural urban migration Societal aging urbanization and agricultural labor force shortage compound these challenges The number of agricultural harvestable farms worldwide is limited and the conversion of forests to farmlands will have irreversible consequences on the ecosystem air pollution levels and oxygen production Automation and intelligence have emerged as solutions for addressing this complicated situation Over the past decade China s mechanization rate for crop cultivation and harvest has increased from 57 in 2012 to 72 in 2021 projected to reach 75 by 2025 It is imperative to robustly develop intelligent agricultural machinery and upgrade the industry The United States Germany China and Japan have implemented their smart agriculture development strategies 1 The global agricultural machinery and DOI 10 12398 j issn 2096 7217 2023 03 001 Received date 2023 05 25 Revised date 2023 08 04 Foundation item National Key Research and Development Plan Project 2021YFD2000503 National Natural Science Foundation Project 32171911 First Author QIAN Zhenjie PhD Associate Professor research interests intelligent agriculture E mail zhenjieqian Correspondence Author JIN Chengqian PhD Professor research interests intelligent agriculture E mail jinchengqian 2023 年 Journal of Intelligent Agricultural Mechanization smart equipment market are projected to reach 258 43 billion US dollars in 2027 and 135 4 billion US dollars in 2026 respectively Intelligent agricultural equipment integrates advanced machinery perception decision control big data cloud plat and the Internet of Things IoT facilitating agricultural tasks autonomy efficiency safety and reliability 2 3 This study explores the technological frontier and development trend of unmanned farm systems Section 1 introduces these systems Section 2 describes the autonomous positioning and navigation technology Section 3 examines the ination sensing technology in unmanned farms including online sensors for combine harvesters and common parameters Section 4 discusses the intelligent control technology in unmanned farms encompassing ination perception technology of working obstacles path planning decisions technology multiple machines cooperative technology assisted driving driverless systems and unmanned farm intelligent management Section 5 discusses the future trend of unmanned farms Finally Section 6 concludes with the findings of this paper and the future development of unmanned farms 1 Unmanned farm systems Figure 1 illustrates an unmanned farm system Unmanned farm refers to an innovative agricultural production model that integrates machinery agronomy and ination through technical means such as the IoT 4 big data artificial intelligence 5 and unmanned agricultural machinery This approach realizes autonomous decision making unmanned production and operation management with data drive without human presence within the farming area Integrating unmanned technology with intelligent machine technology of agricultural equipment allows for intelligent unmanned operation of agricultural equipment Agricultural equipment operates within an unstructured environment that is characterized by diverse and complex interference factors for control systems Achieving unmanned operation necessitates the tractor host to achieve unmanned driving and demands the study of intelligent decision control technologies for various machines 6 7 The integrated solution for unmanned farms which shows in Figure 2 encompasses an unmanned farm control plat comprising meteorological monitoring soil assessment disease pest and weed tracking and seedling growth monitoring This plat interfaces with various unmanned agricultural machinery including tillage transplanting crop protection harvesting and irrigation In the complex working environment of farmland there are common problems such as large labor amount low efficiency difficulty in monitoring operation parameters and inability of adaptive adjustment in operation process This unmanned farm includes intelligent and unmanned key technology research and development and system integration and realized the efficient unmanned walking and intelligent autonomous operation of the whole process of machinery from hangar to field Specific technologies include 1 Online monitoring technology of agricultural machinery operation parameters in the whole process of tillage planting pipe and harvest including tillage depth Figure 1 Schematic of unmanned farm systems 2第 3 期 QIAN Zhenjie et al Development status and trends of intelligent control technology in unmanned farms sowing amount fertilizer application amount drug application amount grain loss rate grain crushing rate grain impurity rate yield etc 2 Electromechanical hydraulic integrated adjustable operating parts and adaptive intelligent control technology including suspension self balancing intelligent control system variable sowing and fertilization intelligent control system variable application intelligent control system chassis all direction leveling intelligent control system cutting table copying intelligent control system adaptive threshing and cleaning intelligent control system 3 Unmanned autonomous operation control technology including navigation automatic driving technology agricultural machinery highly adaptive integration technology unmanned walking and autonomous operation collaborative control technology 2 Autonomous positioning and navigation technology Autonomous positioning and navigation primarily encompass global navigation satellite system GNSS and visual positioning technologies The real time kinematic RTK is a carrier phase difference technology with precision 2 5 cm RTK global positioning system RTK GPS and RTK BeiDou navigation satellite system RTK BDS are widely applied in precision agriculture promoting the development of automatic navigation technology for agricultural equipment Based on foreign inertial measurement unit IMU 3D attitude measurement and compensation technology the positioning accuracy of high precision positioning technology can reach 1 cm when the slope is 15 China s IMU 3D technology achieves a positioning accuracy of 1 cm at a 10 slope This technology is foundational for the automated driving of agricultural machinery therefore this study focuses on location positioning and line detection The current agricultural machinery satellite navigation and positioning technology is relatively mature and standard in high end agricultural machinery Vision and laser based line detection particularly the environmental robustness of visual line detection affects its practical application In the future the combination of satellite positioning and visual navigation will meet the needs of more operation scenarios KAIZU et al 8 used augmented reality AR technology to construct 3D images of the environment to determine the position of tractors HU et al 9 proposed a cascaded navigation control for straight path tracking MALAVAZI et al 10 used lidar to extract lines from 2D point clouds using the PEARL algorithm CHOI et al 11 developed a visual line detection based on morphological features including leaf and stem orientations and crop density ZHANG et al 12 proposed the concept of a vision based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots which can fuse the edge contour and the height ination of rows of crop in images to extract the navigation parameters High precision path Figure 2 Integrated solutions for unmanned farms 32023 年 Journal of Intelligent Agricultural Mechanization tracking control technology in US and China shows the linear tracking control accuracy to be 2 5 3 0 cm at the operating speeds of 25 km h and 12 km h respectively Advancements in the vehicle kinematics model and speed compensation algorithm are needed In US multi sensor fusion navigation technology uses satellite and machine vision fusion positioning technologies to achieve high precision and full width harvest under independent operating conditions In contrast in China the crop boundary identification technology uses satellite positioning and machine vision ination fusion technology which needs further advancement 3 Ination sensing technology in unmanned farms 3 1 Online professional sensors of combine harvesters China s intelligent technology for combine harvesters faces challenges in operation parameter monitoring automatic control navigation and auxiliary driving Although exploratory research exists on online operation parameter detection mature industrial products are lacking due to underdeveloped time intensive and costly technologies such as fast and accurate segmentation and recognition of feature ination universal estimation model reconstruction of yield map and error analysis China still employs manual mechanical control of driving speed sorting and cutting and other operating parameters lacking a comprehensive terminal for data collection and display of operating conditions and engine and machine operating status Research on intelligent technology based on closed loop control of job ination is still in the initial stage in China In addition navigation and automatic driving technology based on the harvest operation environment remains challenging The online professional sensors of combine harvesters such as loss rate sensor impurity content sensor and yield diagram are shown in Figure 3 Online grain crushing rate monitoring image processing technology is mainstream in US and Germany with ongoing promotion and application In contrast domestically research is in the prospective stage with no mature technology image online rapid recognition algorithm or crop model JIN et al 13 employed an enhanced U net network for online soybean grain crushing rate and impurity rate detection during mechanical harvesting Online grain loss rate monitoring Foreign use of piezoelectric impact effect principle is mature in harvester products focusing on qualitative analysis domestic theory and model research stage Domestically there is no mature technology and research on sensor superposition signal processing and loss rate distribution model is insufficient HIREGOUDAR et al 14 employed an artificial neural network to assess grain harvest losses under field conditions ZHANG et al 15 improved Deeplabv3 by constructing MobileNetv2 in coding layer and adding ECA efficient channel attention to Encoder and Decoder to improve extraction accuracy of high dimensional features in images with a large number of objects with random state which shows in Figure 4 Online grain moisture content monitoring Foreign systems are mature and commercialized with 95 Figure 3 Loss rate sensor impurity content sensor and yield diagram 4第 3 期 QIAN Zhenjie et al Development status and trends of intelligent control technology in unmanned farms measurement accuracy In contrast the domestic online detection equipment for combine harvesters is underdeveloped Moisture content models based on environmental factors and mechanical vibration require refinement Online grain yield monitoring Foreign systems are established and commercialized with a measurement error of 5 In contrast domestic research is in the initial stage with large impulse model deviation and low sensor response 16 DA et al 17 used six dual plate differential impact sensors to determine grain yield significantly reducing noise interference caused by vibration HU et al 18 developed a two plate impulse grain flow sensor and its differential vibration suppression circuit to mitigated the body vibration influence on measurement accuracy YANG et al 19 designed an impulse grain combined harvest intelligent yield measurement system and a grain yield measurement system based on computer vision JIN et al 20 employed a grain harvester yield monitoring system based on duty cycle measurement which comprised a photoelectric sensor GPS module data processing unit data storage unit and visualization unit The intelligent soybean combine sensors and system made by the Nanjing Institute of Agricultural Mechanisation Ministry of Agriculture and Rural Affairs which shows in Figure 5 Automatic control technology of grain combine operation Foreign advancements enable online detection of industrial control operation parameters and multi parameter independent regulation Ho
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