The Utilization of DenseNet121for Analyzing and Classification of Leaf Image

Now the issue of agriculture has become a significant concern in our modern times. The growth of crops and plants is hindered by numerous challenges, and one of the most pressing concerns is ensuring their health. Agriculture plays a pivotal role in human life and the economy, and poor management practices can lead to significant losses in agricultural output. To address this problem, a deep neural network approach for detecting plant leaf diseases has been developed as an alternative to the Densenet121 neural network. This can easily detect the disease of plant leaf. First select the plant village dataset and apply into pre-processing method. In this part it is very useful to identify the disease, then it will process into model selection and classification. In classification it will train the dataset and the disease can analyse and show the status of plant leaf that is healthy or unhealthy. The disease can be detected in the image of plant leaf. In this study, a publicly available dataset of both healthy and diseased plants is employed to classify crop species and identify the presence of various illnesses across different classes. To achieve this, Densenet121 is utilized. The predicted result based on accuracy