Wheat is a really important food crop worldwide, but diseases from things like fungi, bacteria, and viruses often mess with how much we can grow. Spotting these diseases early is super important so we don't lose a ton of wheat and mess up the food supply. Usually, people check for diseases by hand, which takes forever, needs a lot of work, and people make mistakes. But lately, machine learning, especially with CNNs, has gotten good at automatically finding diseases in plants. This paper is all about using CNNs to spot wheat diseases right away. It's about sorting and naming wheat diseases by looking at pictures of leaves and stems that are infected. The goal is to give farmers a quick, correct, and easy-to-use way to figure out what's wrong, so they can fix it fast and grow better crops. The results are looking good, and CNNs could really help with finding wheat diseases and protecting how much wheat we can grow.

CNN, Real time analysis, Disease detection, Wheat leaf

The globe is under threat by agriculture, specifically from plant diseases which lowers the production. Wheat is one of the key crops of the world but it is beset by numerous diseases that lower its production and endanger food security. The application of CNNs presents a chance to detect such diseases early, allowing farmers to have the instruments to take preventive measures ad ultimately save the crops. In the end creating a system to detect wheat leaf disease in real time with CNNs is not so much about technology its about assisting others and taking care of everyone. With ongoing research, collaboration and innovation, these systems can be developed to assist farmers all over the world. That will assist them in safeguarding their crops, experiencing better harvests, and building more resilient and sustainable future in agriculture.

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