Real-Time Plant Health Detection Using Deep Convolutional Neural Networks

• Published in: Agriculture (Basel) 2023-02, Vol.13 (2), p.510
• Main Authors: Khalid, Mahnoor, Sarfraz, Muhammad Shahzad, Iqbal, Uzair, Aftab, Muhammad Umar, Niedbała, Gniewko, Rauf, Hafiz Tayyab
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Cell phones; Classification; Computer vision; Datasets; Deep learning; Disease detection; Disease transmission; Electronic devices; Farmers; Leaves; Machine learning; Medical imaging; Mobile devices; Model accuracy; Neural networks; object detection; Object recognition; Pattern recognition; Pesticides; Plant diseases; Plant growth; plant health detection; Plants; precision agriculture; Production methods; Real time; Technology application; Viruses; YOLOv5
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13020510

In the twenty-first century, machine learning is a significant part of daily life for everyone. Today, it is adopted in many different applications, such as object recognition, object classification, and medical purposes. This research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves. Typically, farmers are unaware of diseases on plant leaves and adopt manual disease detection methods. Their production often decreases as the virus spreads. However, due to a lack of essential infrastructure, quick identification needs to be improved in many regions of the world. It is now feasible to diagnose diseases using mobile devices as a result of the increase in mobile phone usage globally and recent advancements in computer vision due to deep learning. To conduct this research, firstly, a dataset was created that contained images of money plant leaves that had been split into two primary categories, specifically (i) healthy and (ii) unhealthy. This research collected thousands of images in a controlled environment and used a public dataset with exact dimensions. The next step was to train a deep model to identify healthy and unhealthy leaves. Our trained YOLOv5 model was applied to determine the spots on the exclusive and public datasets. This research quickly and accurately identified even a small patch of disease with the help of YOLOv5. It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. This research is beneficial for farmers since it allows them to recognize diseased leaves as soon as they noted and take the necessary precautions to halt the disease’s spread. This research aimed to provide the best hyper-parameters for classifying and detecting the healthy and unhealthy parts of leaves in exclusive and public datasets. Our trained YOLOv5 model achieves 93 % accuracy on a test set.