MAIZAPP

 

Common rust is a fungal disease that affects maize crops and can have a significant impact on agricultural production. Early detection of this disease is crucial for taking preventive measures and mitigating its spread.

In this context, a mobile application has been developed that utilizes computer vision techniques and convolutional neural networks, particularly the YOLO (You Only Look Once) model, to identify the presence of common rust on maize leaves.

The process of creating the application consists of several stages. Firstly, images of maize leaves affected by common rust are collected and labeled, along with images of healthy leaves as reference. These images are used to train the YOLO model, which is a specialized neural network for object detection and localization in images.

During training, the model learns to recognize the visual patterns associated with common rust on maize leaves. Once the training is complete, the model is optimized and adjusted for implementation on mobile devices, so that it can run efficiently on a phone or tablet.

The resulting mobile application allows farmers or interested users to capture images of maize leaves using the camera of their mobile device. The application then sends the image to the YOLO model, which analyzes the image in real-time and detects the presence of common rust on the leaf.