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Answer: Develop an image segmentation ML model to identify the boundaries of the rust spots, enabling precise detection of the disease's presence and a detailed assessment of its severity by analyzing the segmented areas.
Image segmentation is the optimal choice for this task because it involves dividing an image into segments to identify objects within it. By applying an ML model for segmenting rust spots, the team can precisely detect the disease's presence and gauge its severity through the analysis of the segmented areas. This approach is more effective than template matching, object detection, or image classification for accurately predicting the presence and intensity of the disease, given the need to assess the disease's severity based on the shape and size of the rust spots.
Author: LeetQuiz Editorial Team
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As an ML engineer on an agricultural research team, you are tasked with developing a solution to detect leaf rust spots in crop images, where the shape and size of the spots can indicate the severity of the disease. The solution must accurately predict the presence and intensity of the disease to assess its severity effectively. Considering the need for precision in identifying the disease's boundaries and the varying sizes of rust spots, which of the following approaches would be the most effective? (Choose one correct option)
A
Develop a template matching algorithm using traditional computer vision libraries, which is efficient for detecting known patterns but may not adapt well to the variability in rust spot appearances.
B
Create an object detection model that can pinpoint the rust spots, useful for locating objects within images but may not provide detailed information on the disease's intensity.
C
Develop an image classification ML model to determine the presence of the disease, which can classify images but lacks the granularity to assess disease severity accurately.
D
Develop an image segmentation ML model to identify the boundaries of the rust spots, enabling precise detection of the disease's presence and a detailed assessment of its severity by analyzing the segmented areas.
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