AI-Driven Smart Agriculture Platform for Integrated Crop Health Monitoring and Yield Optimization: A Systematic Literature Review

2026-09-14T08:24:47+00:00

Agriculture is undergoing rapid transformation through the integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Internet of Things (IoT) technologies. These advancements enable intelligent crop disease detection, crop yield prediction, and precision farming practices that improve agricultural productivity while minimizing resource consumption. Recent studies have demonstrated significant improvements in disease classification accuracy using Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and multimodal learning techniques. Similarly, advanced regression and ensemble learning algorithms have enhanced crop yield prediction by utilizing climatic, soil, and environmental parameters. This systematic literature review analyzes recent research published between 2021 and 2026 on AI-driven smart agriculture systems. The review compares various machine learning algorithms, datasets, evaluation metrics, and their advantages and limitations reported in the literature. Furthermore, the study identifies current research gaps and discusses future research opportunities involving Explainable Artificial Intelligence (XAI), multimodal learning, federated learning, and real-time agricultural decision support systems. The findings indicate that integrating multiple AI techniques into a unified smart agriculture platform can significantly improve prediction accuracy, transparency, and practical usability for modern precision farming.

Automated Detection and Prioritization of Civic Infrastructure Complaints Using a Cascaded YOLOv8–LLM Pipeline with Geospatial Priority Scoring on Serverless AWS

2026-09-25T10:24:05+00:00

The systems used for lodging complaints by the municipalities in most Indian cities involve manual indenting, which leads to delays, inaccurate information, and wrong routing of complaints to departments. This paper presents a serverless web application that automates all the stages in the civic complaint life cycle, which prolongs the whole process. In this system, the complaint is submitted through images only, and the citizens do not need to define or classify the complaint. The important aspect of this system is that it works based on the seven-phase AI pipeline, which incorporates a specially trained YOLOv8 detector to determine 4 most common types of complaints: potholes, garbage dumps, waterlogging, and faulty streetlights. In case of low confidence prediction can always fall back on Amazon Nova Lite, a multimodal large language model that can be accessed using Amazon Bedrock. Furthermore, the pipeline also performs the assessment of the severity of the complaint, automatic routing to a department, generation of complaint descriptions, and priority scoring. The backend part of the application is executed by means of AWS Lambda, S3, API Gateway, and DynamoDB. The mentioned YOLOv8 has an mAP@50 indicator equal to 0.75 for all 4 classes.

Journal of Computer Science Engineering and Software Testing

AI-Driven Smart Agriculture Platform for Integrated Crop Health Monitoring and Yield Optimization: A Systematic Literature Review

Automated Detection and Prioritization of Civic Infrastructure Complaints Using a Cascaded YOLOv8–LLM Pipeline with Geospatial Priority Scoring on Serverless AWS