Harnessing DenseNet-121 for Intelligent Paddy Crop Disease Management

The economy of any country and the ability of its people to live well rely a lot on farming. So, keeping farming successful depends on stopping plant diseases. This work suggests a method to make the best use of resources to improve farming results by finding diseases effectively. We use a Dense Convolutional Neural Network (DCNN), specifically DenseNet-121, for this classification task. The work uses the powerful feature extraction abilities of a pre-trained DenseNet-121 model from Keras. By using a filter many times on an input, a convolution creates a feature map that highlights where and how strong certain features are in images. Convolutional networks allow us to automatically learn many different filters that are specially designed for the training data. This method helps grow more crops, which not only allows us to find plant diseases better but also boosts the country's economy. Using this algorithm well shows that it can help keep crops healthy and increase farm productivity.