Monarch Tractor sued over tractors that were ‘unable to operate autonomously’

TechCrunchTuesday, November 18, 2025 at 6:54:54 PM
  • Burks Tractor has initiated legal proceedings against Monarch, alleging that the company's tractors do not meet promised autonomous operation capabilities and are defective. The lawsuit underscores ongoing operational issues with the ten tractors purchased, raising questions about Monarch's product reliability.
  • This lawsuit could have significant implications for Monarch, potentially affecting its reputation in the agricultural technology sector and raising concerns among potential customers about the viability of its autonomous solutions.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
Judge dismisses lawsuit twice due to alleged deepfake video testimony
NegativeArtificial Intelligence
A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.
MAT-MPNN: A Mobility-Aware Transformer-MPNN Model for Dynamic Spatiotemporal Prediction of HIV Diagnoses in California, Florida, and New England
PositiveArtificial Intelligence
The study introduces the Mobility-Aware Transformer-Message Passing Neural Network (MAT-MPNN) model, designed to enhance the prediction of HIV diagnosis rates across California, Florida, and New England. This model addresses the limitations of traditional Message Passing Neural Networks, which rely on fixed binary adjacency matrices that fail to capture interactions between non-contiguous regions. By integrating a Transformer encoder for temporal features and a Mobility Graph Generator for spatial relationships, MAT-MPNN aims to improve forecasting accuracy in HIV diagnoses.
Probabilistic Wildfire Susceptibility from Remote Sensing Using Random Forests and SHAP
PositiveArtificial Intelligence
Wildfires are a major global threat, particularly in California, where they are influenced by climate, topography, vegetation, and human activities. This study develops a wildfire risk map for California using the random forest algorithm enhanced by Explainable Artificial Intelligence (XAI) through Shapley Additive Explanations (SHAP). The model showed strong predictive performance, particularly for grasslands and forests, with high AUC values. Validation strategies indicated moderate transferability and enhanced generalization for forest areas, highlighting the model's effectiveness in predic…
Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis
PositiveArtificial Intelligence
Traffic safety analysis at signalized intersections is crucial for minimizing vehicle and pedestrian collisions. Traditional crash-based studies face challenges due to data sparsity and latency. This paper introduces a multi-camera computer vision framework for real-time safety assessment through Post-Encroachment Time (PET) computation, tested at the intersection of H Street and Broadway in Chula Vista, California. Utilizing four synchronized cameras, the system processes frames on NVIDIA Jetson AGX Xavier devices with YOLOv11 segmentation for vehicle detection, creating a unified bird's-eye …