Game-changing technology figures to revolutionize weed control. FARMING MEETS SCI-FI: The LaserWeeder G2 builds on the success of its predecessors to bring submillimeter weed control to a wider range of farms, crops and soil types. CHIPS ACT: Former Vice President Kamala Harris was roasted for delivering another "word salad" on a public stage after trying to tie the "innovation" of Big Tech to her love of nacho cheese Doritos during an artificial intelligence conference. FRONT-FLIPPING ROBOT: Chinese robotics company Zhongqing Robotics, also known as EngineAI, has officially entered the humanoid robotics scene by releasing a video showcasing what it claims is the world's first humanoid robot front flip. FIGHT TO SAVE KIDS: Australia's Murdoch Children's Research Institute is helping scientists use stem cell medicine and artificial intelligence to develop precision therapies for pediatric heart disease, the leading cause of death and disability in children.

Game-changing technology figures to revolutionize weed control. Imagine a future where farming is not only more efficient but also cleaner and greener. That's exactly what Carbon Robotics is promising with its latest innovation, the LaserWeeder G2. This game-changing technology figures to revolutionize weed control by ditching chemicals altogether. It's a solution that's both good for the planet and great for farmers looking to reduce their environmental footprint.

Robotic weed flaming is a new and environmentally friendly approach to weed removal in the agricultural field. Using a mobile manipulator equipped with a flamethrower, we design a new system and algorithm to enable effective weed flaming, which requires robotic manipulation with a soft and deformable end effector, as the thermal coverage of the flame is affected by dynamic or unknown environmental factors such as gravity, wind, atmospheric pressure, fuel tank pressure, and pose of the nozzle. System development includes overall design, hardware integration, and software pipeline. To enable precise weed removal, the greatest challenge is to detect and predict dynamic flame coverage in real time before motion planning, which is quite different from a conventional rigid gripper in grasping or a spray gun in painting. Based on the images from two onboard infrared cameras and the pose information of the flamethrower nozzle on a mobile manipulator, we propose a new dynamic flame coverage model. The flame model uses a center-arc curve with a Gaussian cross-section model to describe the flame coverage in real time. The experiments have demonstrated the working system and shown that our model and algorithm can achieve a mean average precision (mAP) of more than 76\% in the reprojected images during online prediction.

Organic weed control is a vital to improve crop yield with a sustainable approach. In this work, a directed energy weed control robot prototype specifically designed for organic farms is proposed. The robot uses a novel distributed array robot (DAR) unit for weed treatment. Soybean and corn databases are built to train deep learning neural nets to perform weed recognition. The initial deep learning neural nets show a high performance in classifying crops. The robot uses a patented directed energy plant eradication recipe that is completely organic and UV-C free, with no chemical damage or physical disturbance to the soil. The deep learning can classify 8 common weed species in a soybean field under natural environment with up to 98% accuracy.

Precise robotic weed control plays an essential role in precision agriculture. It can help significantly reduce the environmental impact of herbicides while reducing weed management costs for farmers. In this paper, we demonstrate that a custom-designed robotic spot spraying tool based on computer vision and deep learning can significantly reduce herbicide usage on sugarcane farms. We present results from field trials that compare robotic spot spraying against industry-standard broadcast spraying, by measuring the weed control efficacy, the reduction in herbicide usage, and the water quality improvements in irrigation runoff. The average results across 25 hectares of field trials show that spot spraying on sugarcane farms is 97% as effective as broadcast spraying and reduces herbicide usage by 35%, proportionally to the weed density. For specific trial strips with lower weed pressure, spot spraying reduced herbicide usage by up to 65%. Water quality measurements of irrigation-induced runoff, three to six days after spraying, showed reductions in the mean concentration and mean load of herbicides of 39% and 54%, respectively, compared to broadcast spraying. These promising results reveal the capability of spot spraying technology to reduce herbicide usage on sugarcane farms without impacting weed control and potentially providing sustained water quality benefits.

Climate change, increase in world population and the war in Ukraine have led nations such as the UK to put a larger focus on food security, while simultaneously trying to halt declines in biodiversity and reduce risks to human health posed by chemically-reliant farming practices. Achieving these goals simultaneously will require novel approaches and accelerating the deployment of Agri-Robotics from the lab and into the field. In this paper we describe the ARWAC robot platform for mechanical weeding. We explain why the mechanical weeding approach is beneficial compared to the use of pesticides for removing weeds from crop fields. Thereafter, we present the system design and processing pipeline for generating a course of action for the robot to follow, such that it removes as many weeds as possible. Finally, we end by proposing a trust-based ladder of autonomy that will be used, based on the users' confidence in the robot system.

Convolutional neural networks are one example of deep learning. Other examples of the use of deep learning include natural language processing and recommender systems. Deep learning applications are becoming more widespread, from improving worker safety around heavy machinery to speech translation and hearing assistance. CNNs are the brains behind home assistance devices. CNNs use tens or hundreds of layers of hidden layers to learn to recognize the different features of an image.

The agricultural marketplace -- and where farmers will be willing to spend their money on AI and ML technology -- will ultimately decide the winners and losers. But there are things agriculture companies can do to position themselves to take the lead, and it all starts with the kind of purpose that drove John Deere to become one of the most well-known names in a global industry for centuries. But first, what's AI got to do with agriculture? For crop producers, data has changed how they do business. It's been both a blessing and a curse since precision ag technology entered the industry around 25 years ago.

Robotic weed control has seen increased research in the past decade with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for arable croplands, ignoring the significant weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust detection of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the highly complex Australian rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust detection methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper also presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification performance of 87.9% and 90.5%, respectively. This strong result bodes well for future field implementation of robotic weed control methods in the Australian rangelands.