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Plant diseases are a major threat to food security and economic prosperity around the globe. Deep learning models based on Convolution Neural Network (CNN) have shown promising results in dealing with plant disease detection tasks. However, according to the No Free Lunch Theorem, no single model is suitable for all cases. Moreover, the vast diversity of plant diseases makes the model selection process time and resource extensive, using exhaustive search. This work proposes a meta-learning-based framework that recommends top-n suitable models for an unseen plant disease detection dataset using the prior evaluations of benchmark models on plant disease detection tasks. Rank-Biased Overlap (RBO) is used to evaluate the efficacy of the proposed framework by evaluating actual rankings with respect to the predicted rankings. Extensive comparative experiments are carried out with different configurations of meta-extractors and meta-learners. The results obtained demonstrate that the probe network trained for 10 epochs (termed as "intermediate stage") along with standard deviation as meta-extractor and Support Vector Regressor as the meta-learner outperforms the rest with average RBO scores of 0.76, 0.73 and 0.75 for Top-5, Top-3 and Top-1 recommendations, respectively. Overall, this paper presents a viable substitute for the exhaustive search process carried out for choosing the best deep learning model for plant disease detection scenario, leading to better resource utilization and faster implementation procedure. • Meta-learning framework to recommend models for plant disease identification task. • Meta-dataset created using 13 benchmark CNN models on 24 different species of plants. • RBO score used to evaluate combinations of meta-extractors and meta-learners. [ABSTRACT FROM AUTHOR]