The dairy industry, a vital component of rural economies, faces numerous operational challenges. These include the reliance on manual record-keeping, leading to errors and inefficiencies, inconsistent milk pricing that fails to account for quality variations, and frequent disputes over payments and cow feed allocations. Such issues strain relationships between farmers and dairy owners, hindering trust and productivity. This project presents the Dairy Transaction Management System, a comprehensive digital platform designed to transform how dairy operations are managed. The platform automates key processes such as tracking milk deliveries, standardizing milk prices based on fat content, managing payments, and allocating cow feed. By integrating real-time updates and transparent record-keeping, the system fosters trust and reduces operational friction. Built with scalability in mind, the platform leverages modern technologies to address the unique challenges of rural stakeholders. Its intuitive interface ensures accessibility for users with varying levels of technical expertise, making it a practical solution for improving efficiency in dairy supply chains. Beyond its immediate impact, the system also lays the groundwork for future expansion into managing other commodities, demonstrating its potential to revolutionize rural commerce.

Purpose: Diabetes can lead to complications such as diabetic nephropathy, diabetic neuropathy, and diabetic macular edema (DME). Like DME, AMD-related vision loss can also be prevented through early detection and prompt treatment. Optical coherence tomography (OCT) is the most widely used imaging technique in ophthalmology for diagnosing these conditions. While OCT is effective for early screening, the increasing volume of OCT scans adds to the workload of ophthalmologists, who must interpret each image. Method: To address this challenge, automated diagnostic screening systems are being actively developed to reduce the burden on eye care professionals. Prolonged diabetic may lead to Macular Edema (DME) or CNV or DRUSEN. Which causes the aberrant development of blood vessels from the choroid layer into the retina. DME, CNV may cause to vision loss. Early detection of the disease and correct treatment are crucial to address the issue of vision loss. Artificial intelligence techniques have been widely adopted in many research work to overcome these limitations. This paper contributes towards development of textual reports, helpful for ophthalmologists. Result and Conclusion: This is the review developed for detecting and analysing DME, DRUSEN CNV using AI techniques over the last decade. Also, we tried to convert the OCT finding into a textual report, very well assessed by ophthalmologist. The system automates the process of analysing OCT images, eliminating the need for manual interpretation by ophthalmologists. This significantly reduces the time required for diagnosis. The system processes the images and generates textual reports using deep learning methods.