{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T01:33:32Z","timestamp":1775612012078,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture.<\/jats:p>","DOI":"10.3390\/informatics12040138","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:35:51Z","timestamp":1765190151000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI-Enabled Intelligent System for Automatic Detection and Classification of Plant Diseases Towards Precision Agriculture"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2322-8812","authenticated-orcid":false,"given":"Gujju Siva","family":"Krishna","sequence":"first","affiliation":[{"name":"Faculty of Operations and IT, ICFAI Hyderabad, Hyderabad 501203, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7767-2855","authenticated-orcid":false,"given":"Zameer","family":"Gulzar","sequence":"additional","affiliation":[{"name":"Department of AIML, Malla Reddy University, Hyderabad 500100, India"}]},{"given":"Arpita","family":"Baronia","sequence":"additional","affiliation":[{"name":"Department of CSE, Manipal University Jaipur, Jaipur 303007, India"}]},{"given":"Jagirdar","family":"Srinivas","sequence":"additional","affiliation":[{"name":"Department of IT, Matrusri Engineering College, Hyderabad 500059, India"}]},{"given":"Padmavathy","family":"Paramanandam","sequence":"additional","affiliation":[{"name":"Department of CA, BSAR Crescent Institute of Science & Technology, Chennai 600048, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3561-909X","authenticated-orcid":false,"given":"Kasharaju","family":"Balakrishna","sequence":"additional","affiliation":[{"name":"Department of CS and AI, SR University, Warangal 506371, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106128","DOI":"10.1016\/j.asoc.2020.106128","article-title":"An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild","volume":"89","author":"Zhao","year":"2020","journal-title":"Appl. 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