Ruchi Rani

The inefficiencies of traditional parking systems, including manual entry and reliance on sensors, as well as slow payment lines, contribute to congestion and a lower level of user satisfaction. This paper suggests a smart parking management system (SPMS) based on the license plate recognition (LPR) technology using you only look once version 5 (YOLOv5)modelincombination with a QR code based payment system, to automate the parking system. It’s able to detect vehicle license plates in real-time, which means that data doesn’t have to be entered manually, and it also avoids the need for physical sensors, which cuts down on infrastructure costs. With a centralized database, the efficient tracking of vehicles is achieved, and QR-based payment will facilitate contactless payments at exit. Experimental evaluation shows that the proposed system out performs than the other systems, with an accuracy of 98.09% and recall of 98.25% in case of LPR. This system drastically decreases processing time, congestion and improves overall user experience. SPMS is more efficient, scalable, and reliable than traditional and existing smart parking systems. The results show that the proposed method is a cost-effective and practical solution for solving the modern parking management problem.

Precision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods. However, deep learning techniques demand a large quantity of data to train the models for diagnosis and further classification. Few-shot learning has surfaced to remove the drawbacks of deep learning methods. Therefore, the proposed work presents a novel GRCornShot model for corn disease diagnosis using few-shot learning with Prototypical Networks based on metric learning. Metric Learning calculates the distance to measure the similarity between the data points. Hence, addressing the challenge of limited labeled data, GRCornShot effectively classifies healthy and corn diseases. Furthermore, the Gabor filter is incorporated into the backbone network ResNet-50 to extract the texture features and to enhance the classification performance. The experiments show the promising application of few-shot learning in agronomic applications, providing a robust solution for detecting corn diseases precisely with minimal data requirements. Using a 4-way 2-shot, 3-shot, 4-shot, and 5-shot learning strategy, GRCornShot achieves impressive accuracy of 96.19%, 96.54%, 96.90%, and 97.89%, respectively.

Customer segmentation in e-commerce: K-means vs hierarchical clustering

Real time Indian sign language recognition using transfer learning with VGG16

Optimizing multi-tenant database architecture for efficient software as a service delivery

The increased demand for food is accelerating plant diseases globally. Hence, a manual process of detection of plant diseases is almost impossible. Artificial Intelligence (AI) can offer several solutions to many problems of farmers. AI is facile to mitigate farmer’s agriculture challenges. With the unpredictable changing climate, plants are often affected by several diseases where AI can play an important role. AI techniques such as Machine learning and deep Learning have been employed in literature to detect, predict, and design recommendation systems for plant diseases. Significant work has been done in this area in the last two decades, which can change farmer’s lives in the coming years. This paper presents a systematic multi-fold survey and analysis of such work focusing on recent AI techniques developed to combat plant diseases. This article discusses various challenges faced by farmers and their AI solutions. It analyzes several applications of AI in agriculture, current trends, and advancements in plant disease detection. It will serve researchers as a valuable document for further research to solve farmer’s issues.