A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

This paper investigates the obstacles to expanding and developing Bangladesh's agricultural machinery manufacturing and production industry. Due to its fertile soil and hospitable climate, Bangladesh is primarily an agricultural country where many crops are grown in large quantities. For food production to be secure and sustainable in the foreseeable future to meet future demand, mechanization is an indispensable factor. However, the agro machinery industry faces obstacles such as research facilities, financial support, qualified workers, technical support, double taxation, and storage facilities. Government policy can play a crucial role in fostering the growth and expansion of the agro-machinery industry and related communities. The article clearly describes how far away the agro machinery industry in Bangladesh is, discusses recent challenges, and encourages support for promoting mechanization throughout the country and strategies for overcoming future obstacles.

Object detection represents a foundational capability in Artificial Intelligence (AI), enabling machines to interpret visual environments through precise object localization and classification. This comprehensive review chronicles the revolutionary evolution of the You Only Look Once (YOLO) framework from its inception to the state-of-the-art YOLOv12. Beginning with the limitations of classical approaches using handcrafted features, YOLO’s paradigm-shifting is documented transition to unified real-time detection via regression-based architectures. Methodically analyzing each major version (v1- v12), key innovations is detailed including multi-scale predictions (v2/v3), anchor-free designs (v8), programmable gradient information (v9), and attention-enhanced cross-scale fusion (v12). The review establishes how successive iterations systematically addressed critical challenges: reducing computational latency by 47× versus R-CNN variants, improving mAP by 32.7% on COCO benchmarks, and enabling deployment on edge devices. Beyond architectural analysis, comparative performance evaluations is presented across diverse applications—from autonomous driving to medical imaging—demonstrating YOLO’s unprecedented balance of speed (142 FPS) and accuracy (78.4% AP). The paper further examines emerging implementation trends, hardware optimizations, and domain-specific adaptations that cement YOLO’s position as the de facto framework for real-time vision systems. Our review analysis provides both technical and historical context for researchers and practitioners navigating the landscape of modern object detection.

In pharmaceutical industries, the classification of plasmonic, dielectric, and chiral nanorods is traditionally achieved through sophisticated laboratory setups, chemical treatments, and multi-step processes. This study introduces an innovative and unique optical sorting technique for nanorods with significant optical force having ~10-20N ranges, enabling the classification of nanorods based on their distinct optical force responses. Pushing effects are generally experienced by objects interacting with light; however, pulling forces or lateral movements can also be exhibited, depending on the interaction. Notably, unique force-direction behaviors are generated for different nanorods simultaneously using a single experimental setup. The proposed optical sorting mechanism is demonstrated using Chiral Dielectric Hyperbolic Metamaterials (CDHMMs) as a substrate. A linearly polarized plane wave, incident at a 90-degree angle, is used to generate distinct responses from three types of nanorods: SiO₂ (dielectric), chiral nanorods, and Au (plasmonic). Lateral forces are displayed by dielectric and chiral nanorods along distinct axes, with the forces on dielectric nanorods being less pronounced. Plasmonic nanorods exhibit substantial optical pushing forces. These effects are caused by the interplay of material characteristics, such as refractive index, chirality, and plasmonic resonance, with the incident light. Through this approach, a sustainable and environmentally friendly alternative to conventional chemical-based sorting techniques is provided, reducing reliance on toxic substances and expensive processes.