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You are an ML engineer on an agricultural research team tasked with developing a crop disease detection tool. Your goal is to detect leaf rust spots in images of crops to determine the presence and severity of a disease. These rust spots can vary in shape and size, and their characteristics are correlated with the severity of the disease. Accurate detection and analysis of these spots are crucial for implementing effective agricultural interventions. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
You are an ML engineer on an agricultural research team tasked with developing a crop disease detection tool. Your goal is to detect leaf rust spots in images of crops to determine the presence and severity of a disease. These rust spots can vary in shape and size, and their characteristics are correlated with the severity of the disease. Accurate detection and analysis of these spots are crucial for implementing effective agricultural interventions. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
Explanation:
The correct answer is B. An image segmentation ML model is the most appropriate solution for detecting rust spots and assessing their severity. Image segmentation can locate the exact boundaries of the rust spots, allowing for a detailed analysis of their shape and size. This is crucial because the severity of the disease is correlated with the characteristics of the rust spots. Object detection models, while capable of localizing spots, do not provide as detailed information about their boundaries. Traditional template matching algorithms are less effective due to the variability in spot shapes and sizes. Image classification models predict the presence of the disease but do not offer detailed insights into the severity based on spot characteristics.