In a recent project, I used PIL to develop an image recognition system for identifying plant diseases. The main challenge was handling diverse and large datasets of images with varying quality and resolution. To overcome this, I implemented a preprocessing step using PIL’s resize function to standardize the image sizes, improving model training efficiency.
Another issue was dealing with color inconsistencies due to different lighting conditions in the images. I utilized PIL’s convert function to transform all images into grayscale, reducing the impact of these variations on the model’s performance.
The most complex part was creating bounding boxes around diseased areas. Initially, it was difficult to accurately identify these regions due to their irregular shapes and colors. However, by leveraging PIL’s ImageDraw module, I developed an algorithm that could dynamically create bounding boxes based on pixel intensity differences.
