Plant Leaf Disease Detection using Deep Learning

Abstract

Abstract: Pests damage plants and crops, which has an impact on the nation's agricultural output. Typically, farmers or professionals use their own eyes to monitor the plants to look for disease and identify it. However, this approach could be timeconsuming, expensive, and unreliable. Results from automatic detection employing image processing methods are quick and precise. In this study, deep convolutional networks are used to develop a novel method of classifying leaf images in order to recognise plant diseases. The technique of precise plant protection has the potential to grow and improve, and computer vision advancements have the potential to boost the market for applications in precision agriculture. Innovative training methods and the methodology employed make it simple and quick to implement the system in real-world settings. The deep convolutional neural network used in this method paper has been trained and fine-tuned to fit accurately to a database of plant leaves that was gathered independently for various plant illnesses. The innovation and advancement of the proposed model lay in its simplicity; by utilising deep CNN, the model can discriminate between ill and healthy leaves as well as between them and the environment. Healthy leaves and backdrop images are also in line with other classes.