Sustainable Coffee Leaf Diagnosis: A Deep Knowledgeable Meta-Learning Approach

Abstract

Multi-task visual recognition plays a pivotal role in addressing the composite challenges encountered during the monitoring of crop health, pest infestations, and disease outbreaks in precision agriculture. Machine learning approaches have been revolutionizing the diagnosis of plant disease in recent years; however, they require a large amount of training data and suffer from limited generalizability for unseen data. This work introduces a novel knowledgeable meta-learning framework for the few-shot multi-task diagnosis of biotic stress in coffee leaves. A mixed vision transformer (MVT) learner is presented to generate mixed contextual attention maps from discriminatory latent representations between support and query images to give more emphasis to the biotic stress lesions in coffee leaves. Then, a knowledge distillation strategy is introduced to avoid disastrous forgetting phenomena during inner-loop training. An adaptive meta-training rule is designed to automatically update the parameters of the meta-learner according to the current task. The competitive results from exhaustive experimentations on public datasets demonstrate the superior performance of our approach over the traditional methods. This is not only restricted to enhancing the accuracy and efficiency of coffee leaf disease diagnosis but also contributes to reducing the environmental footprint through optimizing resource utilization and minimizing the need for chemical treatments, hence aligning with broader sustainability goals in agriculture.