Revolutionizing Agriculture through Leaf Disease Prediction in Plants

India's agricultural strength is hindered by plant diseases due to limited understanding of disease markers and remedies. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), offers hope for enhanced disease detection in agriculture. Our proposal centers on CNNs for accurate plant disease identification, streamlining the process. Urgency arises from economic repercussions and global food security. Deep learning is employed in various sectors such as healthcare, finance, and agriculture. Its applications range from identifying diseases, classifying weeds and leaves, to predicting soil moisture levels. The survey tackles current trends and objections in deep learning for plant disease prediction, guiding aspiring researchers. Machine Learning (ML) and Deep Learning (DL) tools replace costly visual inspection. Our study deploys ML algorithms including K Nearest Neighbors (KNN), Logistic Regression, Random Forest(RF) algorithm, Naïve Bayes, Decision Trees, and Support Vector Machines(SVM) on a diverse crop image dataset. A CNN-based system, coupled with ML soil analysis, suggests fitting crops, enabling informed planting choices. Classifier comparison validates Support Vector Classifier's (SVC) superiority. Swift disease detection is critical for food demand and economic growth. This study proposes a modern evaluation system using ML and pre-trained models (VGG16, InceptionV3, ResNet152) on datasets to classify plant diseases. IoT further augments disease and yield prediction, vital for modern agriculture.