Constructing agricultural multimedia knowledge bases is complicated by heterogeneous evidence, noisy crowdsourced annotations, variations in sample difficulty, and inadequate integration of multisource information. Using tomato disease and pest data, this study proposes a unified framework comprising Multi-evidence Reliability-Aware Active Crowdsourcing (MRAC) and Ontology-Constrained Multi-Evidence Clustering and Refinement (OMCR). MRAC jointly models annotator reliability, sample difficulty, content-evidence quality, and evidence availability. It selects sample-annotator pairs according to the expected information gain per unit cost and produces calibrated soft-label posteriors. OMCR integ... More >