Decoding Sunflower Downy Mildew: Leveraging Hybrid Deep Learning for Scale Severity Analysis

Growing sunflowers (Helianthus annuus) is very important in farming around the world. But it faces a big problem from downy mildew - mostly caused by Plasmopara halstedii. This pathogen brings damage to these beautiful flowers' growth and development process where millions all over earth suffer similar conditions just like them as well- known scientists. We need to stop this farming problem. It needs a smart way, so we can quickly find and get rid of sunflower leaf infections from something called downy mildew. This will help us manage the sickness better before it spreads more widely than necessary due to early detection efforts on farm lands known today as crop fields growing certain kinds such cotton etc., where usually grow potatoes only. To meet this need, our research begins a new adventure. It creates a mix of deep learning models that effortlessly uses both Convolutional Neural Networks (CNN) and AdaBoost to sort different types of downy mildew scales in complex ways. Our main plan is to pick out carefully 3300 big pictures that cover many different areas. We will use these images for study. These photos show the different kinds of downy mildew. They include many types and how it is in various places, which helps us understand more about a disease called Downy mildew. This big data set is not just for fun, but it gets painted on by our mix model. It sees small changes and hard things in photos of tiny bugs on plants that cause disease called downy mildew scales. It is been so accurate it could show correct results 98.16%. This numbers win is not only a big number standard; it shows how well the model really can understand and group soft white mildew spots into about four different levels. These are all bad situations. This careful view of badness levels is very important for farming workers. It lets them make their fixes according to how serious and fast-growing the bug problem might be. Our mix method always beats single CNN and AdaBoost models when comparing different models. Accuracy, remembering, and F1 score measures all show that joining deep learning's power to find features with AdaBoost's help for changing collections works together. This comparison shows our mix model does better. It also makes it clear we need combined ways of dealing with the complications of crops and their diseases problems.