Abstract:
I provide a method for real-time detection of Paddy Leaf Disease in my article. Blast, brown
spot, and bacterial leaf blight are among the most prevalent diseases that may harm rice plants
in my nation. Farmers may be losing money due to decreased agricultural yields caused by
harmful pathogens. The phrase "paddy leaf disease" describes a group of illnesses that impact
rice plants. These diseases may be caused by fungi, bacteria, or viruses and because the leaves
to become yellow, wilt, and eventually reduce the crop's output. For this reason, I need to find
that issue quickly so that I may harvest additional crops. To enhance crop health, boost yields,
and decrease economic losses, farmers may use deep learning to detect paddy leaf disease. This
approach is quicker, more accurate, and more consistent than traditional methods of disease
identification and diagnosis in rice plants. Discovering trends and patterns in illness data may
also be aided by this, which can result in fresh perspectives and advancements in the agricultural
sector. I need a procedure, such as data collecting, in order to complete the process or create
that Deep-learning. From the internet, I get a dataset of images. The collection contains five
different kinds of images. I proceed to pre-process the dataset. The next step is to train my
model and put it through its paces in testing. Lastly, I use a number of algorithms, including
DenseNet121, ResNet50V2, MobileNetV2, and EfficientNetB2. Using EfficientNetB2, I was
able to get an accuracy of about 91%. The techniques used in each person's comparison
statements are also included in the article's implementation component. To build the most
accurate model for the conditions, this study also makes use of model validation techniques.
