Agriculture plays an important role in supplying food globally. While farming, crops and leaves can be at risk of different diseases which leads to production losses. Predicting crop diseases manually can be inaccurate and erroneous. In recent years, various methods were developed based on artificial intelligence and machine learning to detect crop diseases. Early detection of crop disease can improve the quality and productivity of agriculture.
Bhavya Nagrath in his blog proposes a web application to detect and classify crop diseases from the images of leaves. He and his team members built this website by integrating machine learning and image processing techniques.
Intel’s AI Tools and Technologies Used
The website is developed using TensorFlow* Optimizations from Intel and Intel® Tiber™ Developer Cloud.
TensorFlow Optimizations from Intel
Intel collaborates with Google* to upstream most optimizations into the stock distribution of TensorFlow with the newest optimizations and features being released earlier as Intel® Extension for TensorFlow*. These optimizations can be enabled with a few lines of code and will accelerate TensorFlow-based training and inference performance on Intel CPU and GPU hardware.
Bhavya also highlights how Intel Tiber Developer Cloud provides Intel optimized software and hardware flexibility to accelerate AI development.
What’s Next?
We encourage you to check out and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel’s AI Software Portfolio to help you prepare, build, deploy, and scale your AI solutions.
About the Author:
Bhavya Nagrath is currently pursuing his bachelors in Electronics and Communication at Vellore Institute of Technology. He is passionate about IoT, VLSI, artificial intelligence, and machine learning.
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Product Marketing Engineer bringing cutting edge AI/ML solutions and tools from Intel to developers.