Smart Cameras Integrated with Artificial Intelligence (AI) and Human Pose Estimation: A Study

Mayur Saudagar Jadhav — 2025-09-10

From the earliest daguerreotypes to the latest smartphone cameras, photography has always sought to capture moments in time. Yet, despite incredible technological advancements, the act of “taking a photo” often still requires a conscious, manual action, pressing a button, setting a timer, or asking someone else to click. This reliance on human intervention often leads to missed opportunities, awkward timer sprints, or less-than-candid shots. Enter the next evolution: “Smart cameras integrated with artificial intelligence (AI) and human pose estimation”. This groundbreaking convergence empowers cameras to not just passively record light, but to understand the scene, recognize human intent, and capture moments spontaneously and intelligently, often without a single tap or verbal command. This ground-breaking skill is mostly the result of sophisticated AI, particularly deep learning and computer vision. The camera system can now analyse the live video input with previously unheard-of detail thanks to these technologies. Here, human pose estimation is the main attraction. In contrast to simple object detection algorithms that only recognise a “person,” pose estimation systems go further. In order to create a real-time “skeleton” of the human body within the frame, they recognise and track important anatomical points, such as elbows, knees, wrists, and even face features. It determines the best time to “click” on its own, without waiting for a button to be pressed, using pre-established or learnt human pose criteria.

Machine Learning-based System for Sugarcane Disease Detection

Archana P. Kale — 2025-10-30

Sugarcane is a crucial industrial crop for sugar and bioethanol production, and its yield and quality are highly dependent on early disease detection and effective treatment strategies. However, sugarcane production is severely affected by numerous diseases such as red rot, smut, mosaic, and rust, which can lead to significant yield losses. Traditional methods for disease identification rely heavily on manual observation and expert knowledge, which are time-consuming and prone to human error. In recent years, machine learning (ML) and deep learning (DL) approaches have gained prominence in agricultural research due to their ability to automate and improve disease detection accuracy. With advancements in artificial intelligence and remote sensing, precision agriculture has become instrumental in improving crop health management. This paper reviews the development of a machine learning-based system for sugarcane disease detection that integrates convolutional neural networks (CNNs), principal component analysis (PCA), and logistic regression (LR) for robust classification. The proposed system aims to identify major sugarcane diseases from image data while providing an automated advisory module for treatment recommendations. Existing works in sugarcane yield prediction, disease classification, hyperspectral imaging, and robotic precision spraying are reviewed to position the proposed system within current research trends. The review highlights how PCA enhances feature selection and reduces computational complexity, while logistic regression ensures interpretable, efficient classification. The study concludes that the integration of statistical and machine learning techniques can significantly improve disease detection accuracy and support data-driven decision-making in sugarcane farming.

International Journal of AI and Machine Learning Innovations in Electronics and Communication Technology

Smart Cameras Integrated with Artificial Intelligence (AI) and Human Pose Estimation: A Study

Machine Learning-based System for Sugarcane Disease Detection