Vision Based Deep Learning Frameworks for Precision Agriculture and Crop Health Monitoring
Download PDFAbstract
This chapter explores the application of vision-based deep learning frameworks for precision agriculture and crop health monitoring. It addresses the critical need for early and accurate detection of crop diseases and pests to enhance agricultural productivity and sustainability. A novel deep learning framework, “AgroVision-Net,” is proposed, which leverages a combination of Convolutional Neural Networks (CNNs) and transfer learning for robust crop disease classification. The framework is trained and evaluated on a comprehensive dataset of plant leaf images, encompassing various crop types and disease conditions. The experimental results demonstrate the superior performance of AgroVision-Net, achieving a high accuracy in disease identification. The chapter also discusses the integration of this framework with unmanned aerial vehicles (UAVs) for large-scale crop monitoring. The findings highlight the transformative potential of deep learning in modernizing agricultural practices and ensuring global food security.
References
- Fao. The state of food and agriculture 2019: Moving forward on food loss and waste reduction. UN, 2019.
- Priyanka Rastogi, Swayam Dua, Vikas Dagar, et al. “Early disease detection in plants using cnn”. In: Procedia Computer Science 235 (2024), pp. 3468–3478.
- Abhishek Upadhyay et al. “Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture”. In: Artificial Intelligence Review 58.3 (2025), p. 92.
- Sergio Arriola-Valverde et al. “A comparative study of deep learning frameworks applied to coffee plant detection from close-range UAS-RGB imagery in Costa Rica”. In: Remote Sensing 16.24 (2024), p. 4617.
- Anurag Rana and Pankaj Vaidya. “YOLO-based deep learning framework for real-time multi-class plant health monitoring in precision agriculture”. In: Scientific Reports 16.1 (2026), p. 197.
- SP Sudha and JB Loret. “A review on machine learning-based precision agriculture techniques for crop farming monitoring with IOT”. In: Discover Environment 4.1 (2026), p. 10.
- Kishor Chettri, Biswaraj Sen, and Palash Ghosal. “Deep learning for precision agriculture: a systematic review of methods, challenges, and future directions: K. Chettri et al.” In: Knowledge and Information Systems 68.1 (2026), p. 35.
- Nabeel Ahmed and Waleed Farooqi. “Deep Learning in Remote Sensing–Driven Precision Agriculture: A Comparative Review of Models, Data Modalities, and Environmental Applications”. In: Data Modalities, and Environmental Applications.
Deep Learning: Foundations, Advances, and Intelligent Applications
