A Florida man is facing multiple charges after he allegedly stole hundreds of pounds of avocados from a Miami-area grove with the intent to sell the avocados for money to buy his kids Christmas gifts.

GPU prices could follow RAM's big rise Meta is reportedly working on a new AI model called'Avocado' and it might not be open source Mark Zuckerberg has been shaking up the company's AI strategy as it pursues superintelligence. Meta CEO Mark Zuckerberg speaks during an event at the Biohub Imaging Institute in Redwood City, Calif., Wednesday, Nov. 5, 2025. Mark Zuckerberg has for months publicly hinted that he is backing away from open-source AI models. Now, Meta's latest AI pivot is starting to come into focus. The company is reportedly working on a new model, known inside of Meta as Avocado, which could mark a major shift away from its previous open-source approach to AI development.

A new program trained on iPhone photos could curb food waste. Avocados have a carbon footprint that is three times higher than bananas. Breakthroughs, discoveries, and DIY tips sent every weekday. The days of buying a rock-tough avocado in the hopes of avoiding mushy food waste may soon be over. Machine learning researchers at Oregon State University (OSU) recently designed an artificial intelligence program that visually assesses avocado quality and ripeness .

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Feedback has been dimly aware for a while that there is a slew of AI-generated music swamping platforms like Spotify. Our awareness was limited, we confess, because we are so old that we still prefer to listen to CDs. Still, we weren't too surprised when New Scientist's Timothy Revell told us about an indie rock band called The Velvet Sundown that appears to be entirely AI-generated, from their songs, which sound like the beige love-children of Coldplay and the Eagles, to their uncanny-valley Instagram photos, which look like rejected concept art from Daisy Jones & the Six.

Efficient identification of picking points is critical for automated fruit harvesting. Avocados present unique challenges owing to their irregular shape, weight, and less-structured growing environments, which require specific viewpoints for successful harvesting. We propose a geometry-based, semantics-aware viewpoint-planning algorithm to address these challenges. The planning process involves three key steps: viewpoint sampling, evaluation, and execution. Starting from a partially occluded view, the system first detects the fruit, then leverages geometric information to constrain the viewpoint search space to a 1D circle, and uniformly samples four points to balance the efficiency and exploration. A new picking score metric is introduced to evaluate the viewpoint suitability and guide the camera to the next-best view. We validate our method through simulation against two state-of-the-art algorithms. Results show a 100% success rate in two case studies with significant occlusions, demonstrating the efficiency and robustness of our approach. Our code is available at https://github.com/lineojcd/GSNBV

With the growing demand for healthy foods, agricultural product price forecasting has become increasingly important. Hass avocados, as a high-value crop, exhibit complex price fluctuations influenced by factors such as seasonality, region, and weather. Traditional prediction models often struggle with highly nonlinear and dynamic data. To address this, we propose a hybrid deep learning model, TCN-MLP-Attention Architecture, combining Temporal Convolutional Networks (TCN) for sequential feature extraction, Multi-Layer Perceptrons (MLP) for nonlinear interactions, and an Attention mechanism for dynamic feature weighting. The dataset used covers over 50,000 records of Hass avocado sales across the U.S. from 2015 to 2018, including variables such as sales volume, average price, time, region, weather, and variety type, collected from point-of-sale systems and the Hass Avocado Board. After systematic preprocessing, including missing value imputation and feature normalization, the proposed model was trained and evaluated. Experimental results demonstrate that the TCN-MLP-Attention model achieves excellent predictive performance, with an RMSE of 1.23 and an MSE of 1.51, outperforming traditional methods. This research provides a scalable and effective approach for time series forecasting in agricultural markets and offers valuable insights for intelligent supply chain management and price strategy optimization.

Abstract-- This paper addresses the challenge of developing a multi-arm quadrupedal robot capable of efficiently harvesting fruit in complex, natural environments. To overcome the inherent limitations of traditional bimanual manipulation, we introduce the first three-arm quadrupedal robot LocoHarv-3 and propose a novel hierarchical tri-manual planning approach, enabling automated fruit harvesting with collision-free trajectories. Our comprehensive semi-autonomous framework integrates teleoperation, supported by LiDAR-based odometry and mapping, with learning-based visual perception for accurate fruit detection and pose estimation. Validation is conducted through a series of controlled indoor experiments using motion capture and extensive field tests in natural settings. Results demonstrate a 90% success rate in in-lab settings with a single attempt, and field trials further verify the system's robustness and efficiency in more challenging real-world environments.

Robotic fruit harvesting holds potential in precision agriculture to improve harvesting efficiency. While ground mobile robots are mostly employed in fruit harvesting, certain crops, like avocado trees, cannot be harvested efficiently from the ground alone. This is because of unstructured ground and planting arrangement and high-to-reach fruits. In such cases, aerial robots integrated with manipulation capabilities can pave new ways in robotic harvesting. This paper outlines the design and implementation of a bimanual UAV that employs visual perception and learning to autonomously detect avocados, reach, and harvest them. The dual-arm system comprises a gripper and a fixer arm, to address a key challenge when harvesting avocados: once grasped, a rotational motion is the most efficient way to detach the avocado from the peduncle; however, the peduncle may store elastic energy preventing the avocado from being harvested. The fixer arm aims to stabilize the peduncle, allowing the gripper arm to harvest. The integrated visual perception process enables the detection of avocados and the determination of their pose; the latter is then used to determine target points for a bimanual manipulation planner. Several experiments are conducted to assess the efficacy of each component, and integrated experiments assess the effectiveness of the system.

Robot-assisted fruit harvesting has been a critical research direction supporting sustainable crop production. One important determinant of system behavior and efficiency is the end-effector that comes in direct contact with the crop during harvesting and directly affects harvesting success. Harvesting avocados poses unique challenges not addressed by existing end-effectors (namely, they have uneven surfaces and irregular shapes grow on thick peduncles, and have a sturdy calyx attached). The work reported in this paper contributes a new end-effector design suitable for avocado picking. A rigid system design with a two-stage rotational motion is developed, to first grasp the avocado and then detach it from its peduncle. A force analysis is conducted to determine key design parameters. Preliminary experiments demonstrate the efficiency of the developed end-effector to pick and apply a moment to an avocado from a specific viewpoint (as compared to pulling it directly), and in-lab experiments show that the end-effector can grasp and retrieve avocados with a 100% success rate.

We present AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion), a novel navigation approach to address holonomic robot collision avoidance when the degree of cooperation of the other agents in the environment is unknown. AVOCADO departs from a Velocity Obstacle's formulation akin to the Optimal Reciprocal Collision Avoidance method. However, instead of assuming reciprocity, AVOCADO poses an adaptive control problem that aims at adapting in real-time to the cooperation degree of other robots and agents. Adaptation is achieved through a novel nonlinear opinion dynamics design that relies solely on sensor observations. As a by-product, based on the nonlinear opinion dynamics, we propose a novel method to avoid the deadlocks under geometrical symmetries among robots and agents. Extensive numerical simulations show that AVOCADO surpasses existing geometrical, learning and planning-based approaches in mixed cooperative/non-cooperative navigation environments in terms of success rate, time to goal and computational time. In addition, we conduct multiple real experiments that verify that AVOCADO is able to avoid collisions in environments crowded with other robots and humans.