A nonprofit is building a mobile app that lets field workers photograph plant leaves to identify crop diseases. Leaves can be affected by more than one disease at once, and the app must return every disease it detects in a single photo, not just the most likely one. Which computer vision capability should the development team use? (Select TWO.)
Select all correct answers, then click Submit.
Short Explanation
When one leaf can have several diseases at once, you need a vision approach that can return multiple findings from the same photo. Multi-label image classification lets the model attach more than one disease tag, and object detection can find each affected region separately and label it on its own.
Full Explanation
The correct answers are multi-label image classification and object detection. The requirement is to detect multiple co-occurring diseases in one photo rather than forcing a single verdict. Multi-label classification is built exactly for this: it lets the model attach more than one tag to the same image when multiple conditions are present. Object detection also satisfies the need because it locates each affected region separately with its own bounding box and label, which lets the app report multiple distinct disease findings and even show the worker where on the leaf each one appears. Optical character recognition is incorrect because it extracts printed or handwritten text from an image; it has no role in identifying disease patterns on a leaf's surface and would only be relevant if the photos contained text labels, which is not what is described. Single-label image classification is incorrect because it forces the model to pick just one answer per image, which directly contradicts the stated requirement that every disease present in the photo be returned, not just the top match.