Designing Interpretable and Explainable AI Frameworks for Smallholder скачать в хорошем качестве

Designing Interpretable and Explainable AI Frameworks for Smallholder

Publisher: Jack Sparrow Publishers Journal : International Journal of Research and Development in Engineering Sciences (IJRDES) , www.ijrdes.com , e-ISSN: 2582-4201 Title: Designing Interpretable and Explainable AI Frameworks for Smallholder Agriculture Paper Link : https://ijrdes.com/paper-view/designi... DOI : https://doi.org/10.63328/IJRDES-V7RI6P3 Abstract: The world's food supply relies on individual with small holding farmers, but these farmers frequently lack access to sophisticated decision-support systems. The models are difficult to grasp, which is limiting the application of artificial intelligence (AI), despite its promise to increase output through prediction, disease detection, and input optimization. For smallholder farmers in particular, our proposed approach integrates interpretable approaches like decision trees and rule-based categorization with post-hoc explainability techniques like SHAP and LIME to generate explainable AI (XAI) models. Additionally, it develops user-friendly interfaces that enhance clarity through the use of visual, verbal, and contextual cues. Trust, usability, and decision-making assistance are assessed using field appraisals and participatory design approaches. Last but not least, the study establishes a link between AI effectiveness and its understandability, which empowers farmers via transparency and promotes long-term agricultural expansion. Smallholder farmers make critical decisions every day—irrigation, pest control, fertilizer timing, crop choice—often with limited data, limited connectivity, and high risk from weather and market uncertainty. In this video, we explore how to design Interpretable and Explainable AI (XAI) frameworks that farmers and field officers can actually trust, understand, and use. You’ll learn: ✅ Why “high accuracy” is not enough in agriculture decision support ✅ Interpretable-first model design (Decision Trees, Rule Lists, GAM/EBM, Monotonic models) ✅ When to use post-hoc XAI (SHAP, LIME) and how to avoid misleading explanations ✅ How to deliver explanations in local language, voice, icons, and low-literacy friendly formats ✅ Handling uncertainty, data scarcity, and seasonal drift ✅ Fairness & reliability checks across regions, crops, and farmer groups ✅ Turning predictions into actionable advice: “What to do + Why + Confidence” 🎯 Best for: Students | Researchers | AgriTech builders | Extension officers | AI/ML enthusiasts | Policy & development teams 💬 Comment below: What’s more important for adoption—accuracy, explainability, or local language delivery? 👍 Like • 🔔 Subscribe • 📤 Share with Agri/AI learners Chapters (Optional) 00:01 Intro – Why explainable AI matters for smallholders 01:10 Smallholder challenges: risk, data scarcity, trust 02:00 Interpretable-first vs black-box models 03:10 XAI tools: SHAP, LIME, rule extraction 04:30 Uncertainty & confidence in recommendations 04:45 Fairness, reliability, and drift handling 05:00 Human-centered explanation design (local language, icons, voice) 05:45 End-to-end framework summary 06:13 Key takeaways + future scope Keywords / Tags Explainable AI, Interpretable Machine Learning, XAI in Agriculture, Smallholder Farmers, SHAP, LIME, Rule-Based AI, Decision Trees, GAM, EBM, Fair AI, Edge AI, Precision Agriculture, Crop Advisory Systems Hashtags #ExplainableAI #InterpretableAI #MachineLearning #AIForAgriculture #SmallholderFarmers #XAI #AgriTech #DataScience #ResponsibleAI #EdgeAI YouTube Description (Short) In this video, we explain how to build Interpretable and Explainable AI (XAI) frameworks for smallholder farmers—so recommendations are not just accurate, but trustworthy and actionable. Learn interpretable-first models, SHAP/LIME explanations, uncertainty & fairness checks, and local-language delivery for real-world adoption. #ExplainableAI #AIForAgriculture #SmallholderFarmers #XAI #MachineLearning