• venkatesh nayak B
• Dr. Pushpa CN

The detection of wheat leaf diseases plays a crucial role in ensuring the health and productivity of wheat crops, which are vital for global food security. Traditional methods of disease detection in wheat leaves often rely on manual observation, making them time-consuming and prone to human error. In response to these challenges, this research proposes an automated approach for wheat leaf disease detection using advanced technologies. Our system employs computer vision techniques, machine learning algorithms, and deep neural networks to analyze digital images of wheat leaves. High-resolution images are captured using modern imaging devices or drones equipped with multispectral cameras, providing detailed information about the visual and spectral characteristics of the leaves. The dataset comprises a diverse range of images depicting various stages and types of wheat leaf diseases. The proposed system utilizes image processing techniques to preprocess the captured images, extracting relevant features that serve as inputs to the machine learning and deep learning models. Convolutional Neural Networks (CNNs) are particularly effective in discerning intricate patterns and textures associated with different wheat leaf diseases. The trained model can classify and identify the presence of diseases accurately, offering a rapid and reliable means of detection. In addition to disease identification, the system generates detailed maps indicating the spatial distribution and severity of wheat leaf diseases across the field. These maps provide farmers and agricultural experts with valuable insights for targeted intervention and optimized disease management strategies. The proposed automated wheat leaf disease detection system aims to enhance the efficiency and accuracy of monitoring and diagnosis, ultimately contributing to the overall health and yield of wheat crops. This technology-driven approach offers a scalable solution for large-scale agricultural operations, promoting timely responses to potential disease outbreaks and contributing to sustainable and resilient wheat production.

Iot,ML,NDVI,precision agriculture,crop health

"Crop Health Monitoring using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.b82-b89, May-2024, Available :http://www.jetir.org/papers/JETIR2405110.pdf

"Crop Health Monitoring using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppb82-b89, May-2024, Available at : http://www.jetir.org/papers/JETIR2405110.pdf