Deep Plant: Detecting Plant Diseases and Recommending Remedies with Deep Learning

In light of rapid increase in human population and correspondingly increased demands in food production, early disease diagnosis and prevention in crops become imperative. However, conventional and long-established methods reliant on human expertise and lab analysis often prove high cost and inaccessible in undeveloped regions. This study aimsto explore the potential of automated image analysis, leveraging smartphone ubiquity, for crop disease identification. Methods: Recent advancements in automated image analysis for crop disease identification are investigated, comparing deep learning (DL) approaches with classical machine learning (ML) algorithms. Various DL models, including Inception ResNet V2, ResNet 50, and Xception, are evaluated alongside classical ML algorithms. The study employs test accuracy, test loss, recall, precision, and F1 score metrics to assess the effectiveness of each approach. Results: The Xception model emerges as the top performer, achieving a test accuracy of 98.06% with low test loss (0.015),and high precision (92%), recall (94%), and Fl score (92%). Conversely, the recurrent neural network (RNN) model exhibitedinferior performance, with a test accuracy of 82.46%. The study underscores the superiority of DL, particularly exemplified by models like Inception ResNet V2, ResNet 50, and Xception, in automating crop disease detection. Conclusion: The integration of DL techniques into agricultural practices offers a cost-effective and scalable solution to address agricultural challenges. By enabling timely and accurate disease detection, these techniques contribute significantly to safeguarding global food security. Through the adoption of AI-driven solutions, farmers can make informed decisions and implement targeted interventions, ultimately enhancing crop resilience and productivity in the face of evolving agricultural threats.