Artificial intelligence (AI) shows great promise for transforming the agriculture sector and can enable the development of many modern farming practices over conventional methods. Nowadays, AI agents and agentic AI have attained popularity due to their autonomous structure and working mechanism. This research work proposes an agentic AI framework that integrates multiple agents developed for farming land to promote climate-smart agriculture and support United Nations (UN) sustainable development goals (SDGs). The developed structure has four agents: Agent A for monitoring soil properties, Agent B for weather sensing, Agent C for disease detection vision sensing in rice crops, and Agent D, a multi-agent supervisor agent chatbot connected with the other agents. The overall objective was to connect all agents on a single platform to obtain sensor data and perform a predictive analysis. This will help farmers and landowners obtain information about weather conditions, soil properties, and vision-based disease detection so that appropriate measures can be taken on agricultural land for rice crops. For soil properties (nitrogen, phosphorus, and potassium) from Agent A and climate data (temperature and humidity) from Agent B, we deployed the long short-term memory (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A; a 0.27 mean absolute error (MAE) for temperature; and a 2.9 MAE for humidity on the Agent B data. For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively. Overall, the proposed framework demonstrates how agentic AI can be used to transform conventional farming practices into a digital process, thereby supporting smart agriculture.

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications.

Phishing is one of the most dangerous attacks in cybersecurity, which has increased since the introduction of IoT devices, involving attempts to trick users into handing over their passwords and sensitive data. Since most existing detection mechanisms are either nonintuitive or untrusted from the user's perspective, this project attempted to create a phishing detection system that relies on machine learning with explainable AI (XAI). Considering the results of previous studies that stress the importance of accurate and understandable phishing detection models, a five-phase framework was adopted: data collection, data cleaning, data modeling, XAI, and design of an interactive mechanism. The PhishTank dataset was preprocessed to improve model performance by optimizing the feature set and eliminating noise. Random Forest (RF) was selected, which was the best in terms of accuracy, precision, recall, and F1 score compared to Logistic Regression (LR) and Decision Trees (DT) models. LIME and SHAP were used to offer interpretability and present feature importance at the instance and global levels, respectively. Through an engaging mechanism, users can input URLs, obtain predictions regarding possible phishing attempts, and even read explanations, promoting comprehension and trust. This research shows that including XAI can improve not only the efficacy of the phishing detection systems, but also the level of trust that users have in such systems and be the basis for even more robust and more explainable cybersecurity mechanisms.

Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consu... | Find, read and cite all the research you need on Tech Science Press

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety.

The sailfish optimizer is a recent swarm-intelligence-based optimization algorithm which mimics the hunting behavior of sailfish in the ocean. It consists of two types of populations, namely, sailfish and sardine, where sailfish represent the candidate solutions and sardines freely maneuver in the search space. Existing research studies have shown that the sailfish optimizer suffers from limited global exploration capability, with local optimum stagnation and slow convergence speed. To address these limitations, an improved sailfish optimizer, namely, the Multi-Strategy Sailfish Optimizer, is proposed in this study. This improved version incorporates a modified search strategy for both sailfish and sardines, a non-linear attack power parameter, an improved hunting procedure, and a dynamic sardine population. The impact of all suggested improvements is analyzed experimentally. Several experiments with single-objective problems presented at the Congress on Evolutionary Computation in 2022 are performed to prove the effectiveness and efficiency of the proposed algorithm. This improved algorithm is compared with well-known optimization algorithms, such as the whale optimization algorithm, the sine–cosine algorithm, etc., and improved variants of those algorithms. A convergence behavior analysis is also performed using convergence graphs. Furthermore, the significance of the work is statistically validated. The analysis indicates that the Multi-Strategy Sailfish Optimizer performs significantly better than other optimization algorithms. It is also applied to solve the tension/compression spring design problem and the mobile robot path planning problem.