Agronomy, Volume 15, Issue 11
2025 November - 226 articles
Cover Story: Plant-parasitic nematodes threaten global crop production, yet their detection still relies on labor-intensive, expertise-dependent microscopy. Advances in artificial intelligence now offer ways to automate nematode identification, classification, and quantification. This review provides an updated evaluation of machine learning and deep learning approaches, with focus on modern object-detection models such as YOLO. By analysing current techniques, datasets, accuracy, and limitations, the study shows how AI-based image analysis can improve diagnostic efficiency and support sustainable crop protection. The findings highlight the strong potential of deep learning to deliver faster, scalable, and more reliable nematode monitoring, while outlining key challenges that remain for fully automated detection systems. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
- You may sign up for email alerts to receive table of contents of newly released issues.
- PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.