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.

Digital Twins in Agriculture and Forestry: A Review.

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features.

Sensors and Robotics for Digital Agriculture.

The latest advances in innovative sensing and data technologies have led to an increasing implementation of autonomous systems in agricultural production processes [...

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.

Sensor-Driven Human-Robot Synergy: A Systems Engineering Approach.

Knowledge-based synergistic automation is a potential intermediate option between the opposite extremes of manual and fully automated robotic labor in agriculture. Disruptive information and communication technologies (ICT) and sophisticated solutions for human-robot interaction (HRI) endow a skilled farmer with enhanced capabilities to perform agricultural tasks more efficiently and productively. This research aspires to apply systems engineering principles to assess the design of a conceptual human-robot synergistic platform enabled by a sensor-driven ICT sub-system.

Evaluation of anterior cruciate ligament surgical reconstruction through finite element analysis.

Anterior cruciate ligament (ACL) tear is one of the most common knee injuries. The ACL reconstruction surgery aims to restore healthy knee function by replacing the injured ligament with a graft. Proper selection of the optimal surgery parameters is a complex task.

Viscous coalescence of unequally sized spherical and cylindrical doublets.

A coalescence model is developed for pairs of unequally sized particles, assuming surface tension driven flow opposed by viscosity. The flow field is extensional, biaxial for spheres and planar for cylinders. The balance of surface energy and viscous dissipation results in a system of two ordinary differential equations for each of the two doublet shapes studied.

Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments.

During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments.

Investigating dynamic interconnections between organic farming adoption and freshwater sustainability.

As freshwater overexploitation in agriculture is rising, the application of alternative farming practices, particularly in water-scarce areas, is critical for the sustainability of the sector. Organic agriculture constitutes an opportunity for freshwater conservation, further improving biodiversity and human health. Notwithstanding literature efforts on the driving factors of organic farming and the impact of the latter on freshwater resources, a dynamic investigation of the interconnections between organic farming diffusion and freshwater sustainability is lacking.

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.

On the magnetic aggregation of FeO nanoparticles.

Background and objective In-vivo MRI-guided drug delivery concept is a personalized technique towards cancer treatment. A major bottleneck of this method, is the weak magnetic response of nanoparticles. A crucial improvement is the usage of paramagnetic nanoparticles aggregates since they can easier manipulated in human arteries than isolated particles.

Efficient Bone Metastasis Diagnosis in Bone Scintigraphy Using a Fast Convolutional Neural Network Architecture.

(1) Background: Bone metastasis is among diseases that frequently appear in breast, lung and prostate cancer; the most popular imaging method of screening in metastasis is bone scintigraphy and presents very high sensitivity (95%). In the context of image recognition, this work investigates convolutional neural networks (CNNs), which are an efficient type of deep neural networks, to sort out the diagnosis problem of bone metastasis on prostate cancer patients; (2) Methods: As a deep learning model, CNN is able to extract the feature of an image and use this feature to classify images. It is widely applied in medical image classification.

Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation.

Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory modelling helps policy-makers develop dynamic quantitative models for strategising development interventions. The aggregation of knowledge from multiple stakeholders provides consolidated and more reliable results.

Machine Learning in Agriculture: A Review.

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management.