End to End Leaf Detection Using Convolutional Neural Network

Global food security is seriously threatened by crop diseases, yet in many places, it is still difficult to identify them quickly because of a lack of infrastructure. In tackling this problem, recent developments in leaf-based image categorization methods have produced encouraging outcomes. This study investigates how to differentiate between healthy and unhealthy leaves using curated datasets using the Random Forest algorithm. The development of the dataset, feature extraction, classifier training, and classification are the various stages of the implementation process. To efficiently distinguish between diseased and healthy samples, the Random Forest classifier is used to train the dataset, which includes pictures of both diseased and healthy leaves. A significant obstacle to agricultural production, plant diseases have an impact on crop yield and quality. Precision farming and sustainable agriculture depend on the timely and precise identification of leaf diseases. In this work, we present a complete system that automatically detects and classifies leaf diseases using Convolutional Neural Networks (CNN), starting with picture capture. The technology offers a quicker method of identifying leaf illnesses and does away with the requirement for manual feature engineering. Our tests, carried out on several public datasets, show the model's efficacy and achieve high accuracy for a range of illness categories. These results underline the practical uses that might improve crop management and early intervention techniques. Using machine learning on massive datasets that are accessible to the public, this approach offers a scalable solution for detecting plant diseases on a large scale.