Comparative Analysis of Deep Learning Algorithms for Disease Detection in Agriculture

The expeditious evolutions in deep learning have revolutionized various sectors, including agriculture. This paper presents a comparative analysis of different deep learning (DL) algorithms applied to disease detection in agricultural crops. For the detection and classification of disease in the crops, the study evaluates the performance of CNN (Convolutional Neural Networks) RNN (Recurrent Neural Networks) and Hybrid Models. Experimental results demonstrate that while CNNs exhibit high accuracy, hybrid models provide a balance between precision and computational efficiency. The result highlight how early disease detection made possible by deep learning models for enhancing agricultural productivity.