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We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN\u200a+\u200aLSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation.<\/jats:p>","DOI":"10.3233\/jifs-237482","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T11:14:34Z","timestamp":1709291674000},"page":"9709-9726","source":"Crossref","is-referenced-by-count":3,"title":["Integrating 5G and machine learning technologies for advanced PDM in smart farming"],"prefix":"10.1177","volume":"46","author":[{"given":"Weidong","family":"Zhang","sequence":"first","affiliation":[{"name":"Changzhou College of Information Technology, Changzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huadi","family":"Tan","sequence":"additional","affiliation":[{"name":"Changzhou College of Information Technology, Changzhou, Jiangsu, 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