Leveraging Deep Learning for Comprehensive Plant Disease Classification: A Pathway to Sustainable Agriculture

Crop disease classification is a valuable tool in sustaining food security and agriculture the objective of this research was to identify an advanced deep model in the form of recurrent neural networks and convolutional neural networks which could be proved relevant in classifying crop diseases. It outlines methods involved in data collection, preprocessing, feature extraction, selection, training, and evaluation. Gather a large dataset of crop images. Techniques such as data augmentation and normalization are used to enhance the model’s generalization ability by preprocessing. The best performing RNN architectures and CNN models have obtained a validation accuracy of $\mathbf{9 3. 5 3}$ and $\mathbf{9 1. 3 2}$ for training respectively, with improvements observed at different epochs. Model selection focuses on optimizing architectures and hyperparameters through cross-validation and grid search. Training of models is done on the annotated datasets, and their performance is measured in terms of F1-score, recall, accuracy, or precision. Class imbalance and domain shift as well as limited labeled data are other challenges, but targeted strategies may alleviate their impact towards improving deep learning models to classify crop diseases such that early diagnosis and management may come at a better food security and agricultural sustainability base.