AI-BASED CROP HEALTH MONITERING SYSTEM

Agriculture plays a vital role in ensuring food security, and early detection of plant diseases is important to reduce crop loss and improve productivity. The proposed AI based Crop Health Monitoring System enhances plant leaf disease detection using advanced deep learning techniques. Unlike traditional methods that rely only on Convolutional Neural Networks (CNNs), this system uses Vision Transformers (ViTs) with hybrid attention mechanisms to capture both global and local features of leaf images, improving detection accuracy. To address the limitation of labeled agricultural datasets, the system applies self-supervised pretraining on large plant image datasets, reducing the need for manual labeling. In addition, data augmentation techniques and Generative Adversarial Networks (GANs) are used to generate synthetic images and increase dataset diversity. The framework also combines CNN and transformer models through ensemble learning to improve robustness under different environmental conditions. Furthermore, the system supports edge deployment for real-time monitoring and early disease detection in agricultural fields. This approach helps farmers take timely action, reduce crop damage, and improve overall yield and crop management

Artificial Intelligence, Crop Monitoring System, Plant Disease Detection, Deep Learning, CNN, Vision Transformer (ViT), Image Processing, Smart Agriculture, Real-Time Monitoring

1.
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, pp. 1–10, 2016.
2.
A. Dosovitskiy et al., “An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale,” International Conference on Learning Representations (ICLR), 2021.
3.
I. Goodfellow et al., “Generative Adversarial Networks,” Advances in Neural Information Processing Systems (NeurIPS), 2014.
4.
Z. H. Zhou, “Ensemble Methods: Foundations and Algorithms,” Chapman and Hall/CRC, 2012.
5.
A. Kamilaris and F. X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018.
6.
S. Khanal, J. Fulton, and S. Shearer, “An Overview of Current and Potential Applications of Machine Learning in Agricultural Systems,” Computers and Electronics in Agriculture, vol. 151,
7.
pp. 69–81, 2018.
8.
A. Chlingaryan, S. Sukkarieh, and B. Whelan, “Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation,” Computers and Electronics in Agriculture, vol. 151,
9.
pp. 61–69, 2018.
10.
J. You et al., “Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data,” AAAI Conference on Artificial Intelligence, 2017.
11.
X. E. Pantazi et al., “Wheat Yield Prediction Using Machine Learning and Advanced Sensing Techniques,” Computers and Electronics in Agriculture, vol. 121, pp. 57–65, 2016.
12.
A. Crane-Droesch, “Machine Learning Methods for Crop Yield Prediction and Climate Change Impact Assessment,” Agricultural and Forest Meteorology, 2018.