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New Farming Robot Uses AI to Kill 100,000 Weeds per Hour

#artificialintelligence

Robots! Whether you love them or fear them, you can't deny they are pretty useful. One such robot is the autonomous weeder by Carbon Robotics. According to its website page, the robot " leverages robotics, artificial intelligence (AI), and laser technology to safely and effectively drive through crop fields to identify, target and eliminate weeds." "Unlike other weeding technologies, the robots utilize high-power lasers to eradicate weeds through thermal energy, without disturbing the soil. The automated robots allow farmers to use fewer herbicides and reduce labor to remove unwanted plants while improving the reliability and predictability of costs, crop yield, and more."


Killer farm robot dispatches weeds with electric bolts

The Guardian

In a sunny field in Hampshire, a killer robot is on the prowl. Once its artificial intelligence engine has locked on to its target, a black electrode descends and delivers an 8,000-volt blast. A crackle, a puff of smoke, and the target is dead – a weed, boiled alive from the inside. It is part of a fourth agricultural revolution, its makers say, bringing automation and big data into farming to produce more while harming the environment less. Pressure to cut pesticide use and increasing resistance to the chemicals meant killing weeds was the top priority for the farmers advising the robot company.


Russia Claims First AI Powered Robot Harvesters for Sale – TU Automotive

#artificialintelligence

Russia is claiming the first standard production artificial intelligence powered combine harvesters will come to market this month. Autonomous driving technology specialist, Cognitive Pilot, and Bryanskselmash, agricultural equipment manufacturer, have agreed fit automated drive technology to series produced harvesters rolling off the production line from the end of April 2021. The partners plan to expand joint marketing and other activities that will increase the attractiveness of the solution and expand its geographical reach. In another venture, Cognitive Pilot and Rosagroleasing, Russia's largest state-owned agricultural leasing company, have announced first contracts for AI-based agricultural equipment. This will make equipment available to domestic agricultural enterprises, seeking to improve efficiency, including both medium-size and small-size enterprises.


Why robots just can't grow good weed

Mashable

Cannabis farm production is at an all-time high, but it's unlikely that robots will take over the process anytime soon. The stereotypical weed farm is either a sprawling expanse of crop tended to by free-spirited stoners, or a clandestine basement operation built on information gleaned from online forums. Modern cannabis farm facilities, with their climate controlled grow rooms and automatic irrigation techniques, are a stark departure from pop culture's preconceived notions of what a weed farm looks like. Though far more clinical than its cliché predecessor, the modern cannabis farm still does the bulk of cultivation by hand. Few, if any, other agricultural spaces use human labor over that of a machine's to the degree that cannabis farms do, but the quality-driven nature of weed requires fine motor skills and age-old intuition that technology hasn't adapted to yet. While the agricultural industry has relied on machinery for centuries, automation falls short in the cannabis sphere. The rise in states legalizing marijuana and the 2018 Farm Bill that legalized hemp ushered in a "green rush" of farmers who could grow cannabis, and consumers who could finally buy it.


AI and robotics are helping optimize farms to increase productivity and crop yields

#artificialintelligence

Farmers have long struggled with operational optimization and labor concerns. Finding enough labor to get the job done, as well as keeping workers safe is a constant struggle. "There is an immediate need to improve efficiency and reduce costs, especially now that the pandemic has exposed just how fragile the supply chain is," said Suma Reddy, CEO of Future Acres, an agricultural robotics and artificial intelligence company. "We saw shortages in both production and more workers being put at risk when picking specialty crops on a daily basis that have really caused the industry to take a step back and re-examine how we can create greater resiliency in the food chain." One idea is to equip farms with a combination of AI and robotics that can "think through" as well as do some of the physical work of farming.


High precision control and deep learning-based corn stand counting algorithms for agricultural robot

arXiv.org Artificial Intelligence

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with $C_{robot}=1.02 \times C_{human}-0.86$ and a correlation coefficient $R=0.96$. The mean relative error given by the algorithm is $-3.78\%$, and the standard deviation is $6.76\%$. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.


Australian agtech firm Agerris helping farmers with weed management to improve crop yield

ZDNet

A "drone on wheels" robot developed by Australian agtech firm Agerris is the newest addition to the Victorian-based SuniTAFE Smart Farm that will be used to not only support the farm's operations, but also to train technical staff on-site. The Digital Farmhand was developed to help farmers improve crop yield, pest and weed detection, as well as reduce the need for pesticides. Each mobile roving robot runs on solar energy and features navigation sensors, laser sensors, infrared sensors, cameras. It also has an artificial intelligence system that can create weed heat maps, as well as detect each individual crop and determine its yield estimation, plant size, fruit and flowering count. The contract with SuniTAFE is one of several that Agerris has secured over the last few years since it spun out as a commercial entity from the University of Sydney's Australian Centre for Field Robotics in 2019.


5G: New Wave in the IoT Regime

#artificialintelligence

IoT has been radically changing consumer and business landscape over the last few decades. The diverse set of connected devices from a range of verticals needs a unique communication infrastructure. Besides, these connected devices require low power, faster connectivity, and higher security. With the advent of technologies, enterprises have adopted digital transformation to get an edge over their competitors. As a result, new application areas are emerging across a range of verticals, such as industrial automation, smart factories, Machine to Machine (M2M) process control, discrete and process manufacturing, smart grid, smart meters, smart energy, smart lighting, remote patient monitoring, hospital asset tracking, remote diagnosis, remote surgery, warehouse logistics, fleet management, asset tracking, autonomous driving, smart cities, public safety, parking management, video surveillance, smart building, smart retail, environmental monitoring, water management, and crop management.


A Real-time Low-cost Artificial Intelligence System for Autonomous Spraying in Palm Plantations

arXiv.org Artificial Intelligence

In precision crop protection, (target-orientated) object detection in image processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection drones) to the right place to apply the pesticide. Unnecessary application of non-target areas could be avoided. Deep learning algorithms dominantly use in modern computer vision tasks which require high computing time, memory footprint, and power consumption. Based on the Edge Artificial Intelligence, we investigate the main three paths that lead to dealing with this problem, including hardware accelerators, efficient algorithms, and model compression. Finally, we integrate them and propose a solution based on a light deep neural network (DNN), called Ag-YOLO, which can make the crop protection UAV have the ability to target detection and autonomous operation. This solution is restricted in size, cost, flexible, fast, and energy-effective. The hardware is only 18 grams in weight and 1.5 watts in energy consumption, and the developed DNN model needs only 838 kilobytes of disc space. We tested the developed hardware and software in comparison to the tiny version of the state-of-art YOLOv3 framework, known as YOLOv3-Tiny to detect individual palm in a plantation. An average F1 score of 0.9205 at the speed of 36.5 frames per second (in comparison to similar accuracy at 18 frames per second and 8.66 megabytes of the YOLOv3-Tiny algorithm) was reached. This developed detection system is easily plugged into any machines already purchased as long as the machines have USB ports and run Linux Operating System.


A trusty robot to carry farms into the future

#artificialintelligence

Farming is a tough business. Global food demand is surging, with as many as 10 billion mouths to feed by 2050. At the same time, environmental challenges and labor limitations have made the future uncertain for agricultural managers. A new company called Future Acres proposed to enable farmers to do more with less through the power of robots. The company, helmed by CEO Suma Reddy, who previously served as COO and co-founder at Farmself and has held multiple roles and lead companies focused on the agtech space, has created an autonomous, electric agricultural robotic harvest companion named Carry to help farmers gather hand-picked crops faster and with less physical demand. Automation has been playing an increasingly large role in agriculture, and agricultural robots are widely expected to play a critical role in food production going forward.