Crop Quality Prediction with GPU-DLCQPA: Comparing Deep Learning and Traditional Machine Learning Techniques

Smart agriculture, a field combining technology like drones, AI, IoT, and data analytics, aims to address challenges like rising food prices, water shortages, climate change, and labour scarcity. IoT sensors enable real-time crop health mon- itoring, while edge computing ensures timely decision-making. AI algorithms analyze data from sensors and drones to provide insights on crop diseases, pest infestations, and planting times, enabling farmers to make informed decisions, optimize resources, and minimize environmental impact. This paper compares deep learning and traditional machine learning techniques for crop quality prediction in smart agriculture. We proposed GPU- DLCQPA model, which uses GPU acceleration and YOLO classifier for accurate estimation of crop quality features from real-time images. The proposed deep learning model is compared with base classifiers in machine learning like Support Vector Machine (SVM) and Decision Trees. With an accuracy of almost 96% for crop quality prediction, our results show that GPU- DLCQPA performs much better than standard models in terms of accuracy, processing speed, and scalability. The potential of deep learning and GPU-accelerated computing to revolutionize agricultural practices and empower farmers to make data-driven decisions for yield and resource allocation optimization is the main objective of this study.