{"id":45251,"date":"2023-04-11T03:15:36","date_gmt":"2023-04-11T09:15:36","guid":{"rendered":"https:\/\/www.realsenseai.com\/uncategorized-cn\/smart-agriculture-with-realsense-and-directed-machines\/"},"modified":"2023-04-11T03:15:36","modified_gmt":"2023-04-11T09:15:36","slug":"smart-agriculture-with-realsense-and-directed-machines","status":"publish","type":"post","link":"https:\/\/www.realsenseai.com\/cn\/news-insights-cn\/smart-agriculture-with-realsense-and-directed-machines\/","title":{"rendered":"Smart Agriculture with RealSense and Directed Machines"},"content":{"rendered":"\nThe United Nations projects that the world\u2019s population will reach 9.7 billion by 2050, causing global agricultural production to rise 69% between 2010 and 2050. To meet this demand, farmers and agricultural companies are turning to automation. The Smart Agriculture industry was valued at $11.5 billion in 2021 and is expected to reach $24.3 billion by 2028, a compound annual growth rate of more than 11 percent.\n\nAs farmers and renewable energy providers attempt to make up for labor shortfalls with automation and increased mechanization, a new line of autonomous Land Care Robots (LCRs) from Directed Machines offers a cost-effective way to complement the efforts of human workers.\n\nThe LCR is a heavy-duty, solar-electric machine that can perform many types of land care tasks such as mowing, hauling, security monitoring, grading, and plowing, either under operator control or in autonomous mode. Its machine vision system is anchored by two \u00ae RealSense\u2122 D455 cameras, which include an inertial measurement unit (IMU) for refined depth awareness. In addition to navigation, the cameras equip the robots with dynamic obstacle avoidance capabilities, and include an IR illuminator for night time operation.\n\nEnabled with RealSense cameras, Land Care Robots can autonomously manage vegetation, provide security intelligence, and assess the health of plants and site assets, all while towing heavy loads of equipment and supplies. In addition to agriculture, these versatile mechanized assistants are navigating the renewable energy market, where companies need to manage vegetation around solar panels and other equipment assets.\n\n\"LCRThe LCR has found a home in a wide range of vertical markets including golf courses, agricultural farms, solar farms, ranches, and plant nurseries.<\/em>\n

THE OPTIMAL SOLUTION FOR AUTONOMOUS NAVIGATION<\/h4>\nMost autonomous vehicles designed for smart agriculture or renewable energy navigate using global positioning system (GPS) data. Depth cameras are typically used to detect and avoid obstacles, not for navigation. However, GPS navigation is not effective in structured environments, where the GPS signals are often occluded. The LCR autonomously navigates using the RealSense depth cameras in conjunction with programmable Raspberry Pi* 4 RP2040 microcontrollers. There is no need for April tags or QR codes or any other type of localization symbols. The robot perceives its surroundings in real time and navigates accordingly. By using a probabilistic graph-like (topological) approach commonly called simultaneous localization and mapping, the LCR eliminates the additional overhead of first mapping the environment and then storing\/updating localization data..\n\n\u201cTrees and solar panels can interfere with the GPS signal and introduce a lot of positional error,\u201d explains Dan Abramson, co-founder and chief operating officer at Directed Machines. \u201cFor this reason, we use RealSense depth camera D455 as our primary navigation sensors, and GPS devices as our secondary sensors. We can achieve centimeter precision using machine vision, a massive improvement over GPS in challenging environments and vitally important when operating equipment around critical infrastructure.\u201d\n\n\"LCRLCR traveling autonomously on a GPS-occluded forest path map, showing transformed depth image and RGB image.<\/em>\n

A SOPHISTICATED 3D VISION SYSTEM<\/h4>\nDirected Machine\u2019s sensor suite is anchored by two RealSense cameras D455, which include an inertial measurement unit (IMU) for refined depth awareness. There are two data streams for each camera: RGB data and depth perception data. In addition to navigation, the RealSense cameras equip the Land Care Robots with dynamic obstacle avoidance. To improve the stability of the RGB image and the correspondence between the depth and RGB images, the RGB sensor includes a global shutter and is matched to the depth field of view. A compact form factor makes it easy to unobtrusively mount the cameras on the LCR\u2019s solar panel.\n\n\u201cHaving a wide field of view is extremely important because each robot only needs two cameras to operate in all types of topographies and climate zones, including open areas and challenging structured areas,\u201d Abramson explains.\n\nA near infrared projector, also known as an illuminator, enables the camera to capture depth data even in total darkness. Being able to work at night effectively doubles the useful work time of the robot. It allows work to be performed during off hours at parks, golf courses, and other areas that are busy during the day. When placed in sentry mode, the cameras can identify intruders and alert stakeholders. \u201cThe same LCR can mow grass or haul equipment during the day, then do security work at night, accelerating ROI and improving the value proposition for our customers,\u201d Abramson notes.\n

WHY REALSENSE<\/h4>\nAccording to Abramson, LCR customers benefit from RealSense technology due to its high quality and low price. \u201cThe cost\/benefit ratio is better than any other depth cameras out there,\u201d he says. \u201cOur bill of material cost is much lower than if we had used LiDAR sensors, for instance. We can pass on those savings to customers in terms of lower prices.\u201d\n\nIn addition to its comparatively low cost, Directed Machines selected RealSense technology because it has a solid Linux kernel, which integrates well with many OS options available for the Raspberry Pi.\n\nFinally, RealSense technology processes the depth data within the camera, without burdening the robot\u2019s CPU. This is one of the reasons that Directed Machines can use a low cost, highly efficient compute processor such as the Raspberry Pi to interpret the visual data.\n\n\u201cA lot of our competitors rely on software running on an expensive GPU to perform those calculations,\u201d Abramson says. \u201c RealSense technology has already manipulated the data and put it in a usable form by the time it gets to the CPU, so we can use a much more cost-effective Raspberry Pi board.\u201d\n\nRealSense depth cameras<\/a> allow the LCR to navigate using machine vision in structured environments such as orchards and solar farms, as well as unstructured environments like ranches, parks, and golf courses, some of which comprise thousands of acres.<\/strong><\/em>\n\nWatch the video<\/a>: an LCR managing vegetation in the rows of a utility-scale solar farm.\n\n