aceA multimodal context-aware AI recommender for smart farming
Agricultural productivity in developing regions is significantly affected by plant diseases and pest infestations, making early detection and timely intervention essential for improving crop yield and food security. This study proposes a multimodal context-aware AI recommendation framework that integrates maize leaf image analysis and environmental conditions to support intelligent agricultural decision-making. The framework combines a deep learning-based image classification model with weather variables, includ…
Combining leaf image analysis with weather variables classified three maize conditions more accurately than image-only analysis, and an LLM-based engine produced farmer recommendations that scored highly in an automated evaluation.
Weather data alone separated the disease classes poorly because of overlapping environmental characteristics, and recommendations were judged by an LLM evaluator rather than reported field outcomes.
Evidence
- Peer-reviewedPLOS One2026-10-08
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Truvace Impact Record TRV-2026-1706, v1: “A multimodal context-aware AI recommender for smart farming.” Truvace, 2026-10-17. /record/TRV-2026-1706 (accessed at citation time). sha256 1095d91bd7170d9c…
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