Module 7: Model Evaluation and Explainable AI

by NSTC NSTC

July 4, 2026

About Module 7: Model Evaluation and Explainable AI

Module Content

Module 7: Model Evaluation and Explainable AI

9 Lessons

Chapter 7.1: Model Evaluation Metrics

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Lesson 1:  Lesson 7.1.1: Accuracy, precision, recall and F1-score

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Lesson 2:  Lesson 7.1.2: Confusion matrix for crop disease classification

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Lesson 3:  Lesson 7.1.3: ROC-AUC and model comparison

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Chapter 7.2: Explainable AI for Agriculture

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Lesson 1:  Lesson 7.2.1: Importance of explainability in AI-based crop diagnosis

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Lesson 2:  Lesson 7.2.2: Understanding model decisions in hyperspectral classification

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Lesson 3:  Lesson 7.2.3: Using explainability for scientific and agronomic interpretation

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Chapter 7.3: Spectral Feature Importance

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Lesson 1:  Lesson 7.3.1: Identifying important spectral bands

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Lesson 2:  Lesson 7.3.2: SHAP-based model interpretation

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Lesson 3:  Lesson 7.3.3: LIME-based explanation of crop disease predictions

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