Iraqi media broadcast video that reportedly shows Iranian missiles passing through the country. Iran's mission to the United Nations argued that the country's missiles and drones fired toward Israel were justified, warning the U.S. to "stay away." "It is a conflict between Iran and the rogue Israeli regime, from which the U.S. MUST STAY AWAY!," Iran's mission to the United Nations said in a statement. Iran's warning came as U.S. officials confirmed to Fox News that the U.S. military is continuing to shoot down Iranian drones that are headed toward Israel. "U.S. forces in the region continue to shoot down Iranian-launched drones targeting Israel. Our forces remain postured to provide additional defensive support and to protect U.S. forces operating in the region," a U.S. military official said.

Rear Admiral Daniel Hagari of the Israeli Defense Forces updates the public Saturday, April 13 on Iranian UAVs being launched toward the Jewish state. Iran launched dozens of unmanned aerial vehicles (UAV), also known as drones, at Israel on Saturday evening local time after a week of threatening retaliation for an attack on a consulate in Damascus. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." Iran's state-run news agency Mehr News reported that the Islamic Revolutionary Guard Corps (IRGC) had announced the start of the "anti-Zionist operation," which would hit targets in the Palestinian territories, with further details of the operation announced soon.

Rep. Carlos Gimenez, R-Fla., tells'Fox News Live' that China, Russia, North Korea and Iran want to establish a'new world order.' Lawmakers reacted after Iran launched drones from its own territory toward Israel late Saturday, calling for the White House to "stand firm" and "stop coddling Iran." Speaker Mike Johnson pledged America's "full resolve" to stand with Israel. "As Israel faces this vicious attack from Iran, America must show our full resolve to stand with our critical ally," Johnson said in a statement. "The world must be assured: Israel is not alone."

The White House vowed Saturday that the United States' support for Israel's security is "ironclad," pledging to stand with the Jewish state and "support their defense" after Iran launched an aerial drone attack towards the country Saturday afternoon. Iran launched drones from its own territory toward Israel late Saturday, days after its Supreme Leader warned it would hit back in response to an airstrike on the Iranian consulate in Syria that left several generals dead. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." The White House said the president's team "is in constant communication with Israeli officials as well as other partners and allies."

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only ``normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating ``normal" images for training. Evaluation on the Agriculture-Vision data challenge shows a mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-Vision Challenge Dataset

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. A massive missile and drone attack destroyed one of Ukraine's largest power plants and damaged others, officials said Thursday, part of a renewed Russian campaign targeting energy infrastructure. The Trypilska plant, which was the biggest energy supplier for the Kyiv, Cherkasy and Zhytomyr regions, was struck numerous times, destroying the transformer, turbines and generators and leaving the plant ablaze. As the first drone approached, workers hid in a shelter, saving their lives, said Andrii Gota, chairman of the supervisory board of the state company that runs the plant, Centrenergo.

South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers in over 18 countries. JOHANNESBURG - South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers improve crop yields. Although the Cape Town-based company only started nine years ago, it is already operating in 18 countries, with the U.S. being their largest market, followed by South Africa, Australia, Spain and Portugal. Its customers produce tens of millions of tons of fresh produce every year. California is now ground zero for Aerobotics – where the company has the biggest concentration of customers.

In recent years, reports of illegal drones threatening public safety have increased. For the invasion of fully autonomous drones, traditional methods such as radio frequency interference and GPS shielding may fail. This paper proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder UAV. The interceptor multicopter can autonomously detect and intercept intruders moving at high speed in the air. The strapdown camera avoids the complex mechanical structure of the electro-optical pod, making the interceptor multicopter compact. However, the coupling of the camera and multicopter motion makes interception tasks difficult. To solve this problem, an Image-Based Visual Servoing (IBVS) controller is proposed to make the interception fast and accurate. Then, in response to the time delay of sensor imaging and image processing relative to attitude changes in high-speed scenarios, a Delayed Kalman Filter (DKF) observer is generalized to predict the current image position and increase the update frequency. Finally, Hardware-in-the-Loop (HITL) simulations and outdoor flight experiments verify that this method has a high interception accuracy and success rate. In the flight experiments, a high-speed interception is achieved with a terminal speed of 20 m/s.

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring notable sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises $27$ sequences, spanning over $2.5$ hours and collected from four distinct platforms: a handheld suite, wheeled and legged robots, and vehicles. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of $38.7km$. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately $0.3km^2$. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad applicability beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The complete dataset, including sensor data, GT, and calibration details, is accessible at https://fusionportable.github.io/dataset/fusionportable_v2.

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. Russia has started to make steady progress against Ukraine as Kyiv's forces face dwindling ammunition supplies, much to Ukrainian President Volodymyr Zelenskyy's frustration. "There can be no question, Ukraine could be quickly overwhelmed by both men and arms by odds as great as 10 to 1 within weeks without additional U.S. assistance," Kenneth Braithwaite, a former ambassador and former Navy secretary during the Trump administration, told Fox News Digital. "This is a critical juncture in the war and time is of the essence for Congress to act on a comprehensive package," Braithwaite said.