Assessment of Phosphine Resistance in Major Stored-Product Insects in Greece Using Two Diagnostic Protocols.

Post-harvest losses due to insect infestation and spoilage by bacteria and molds pose significant challenges to global cereal production. This study investigates the prevalence of resistance to phosphine, a commonly used grain protection agent, in stored-grain insects. The research, conducted in various storage facilities across Greece, examined 53 populations of key stored-product insect species.

Human-Robot Interaction in Agriculture: A Systematic Review.

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions.

A Survey of Robotic Harvesting Systems and Enabling Technologies.

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks.

Orchard Mapping with Deep Learning Semantic Segmentation.

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e.

Machine Learning in Agriculture: A Comprehensive Updated Review.

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines.