Citation: Salamai, A.A.; Al-Nami, W.T. Sustainable Coffee Leaf Diagnosis: A Deep Knowledgeable Meta-Learning Approach. Sustainability 2023, 15, 16791. https://doi.org/10.3390/ su152416791 Academic Editor: Ripon Kumar Chakrabortty Received: 21 August 2023 Revised: 28 September 2023 Accepted: 28 September 2023 Published: 13 December 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Sustainable Coffee Leaf Diagnosis: A Deep Knowledgeable Meta-Learning Approach Abdullah Ali Salamai 1, * and Waleed Tawfiq Al-Nami 2 1 Department of Management, Applied College, Jazan University, Jazan 82822, Saudi Arabia 2 Department of Computer, Applied College, Jazan University, Jazan 82822, Saudi Arabia; walnami@jazanu.edu.sa * Correspondence: absalamai@jazanu.edu.sa Abstract: Multi-task visual recognition plays a pivotal role in addressing the composite challenges en- countered 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 general- izability 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. Keywords: sustainable; coffee leaf diseases; leaf diagnosis; disease management; artificial intelligence; meta-learning 1. Introduction Multi-task visual recognition is a revolutionary computer vision paradigm that allows training single artificial intelligence algorithms to perform many visual recognition tasks instantaneously. Different from single-task learning, in which independent models are trained for each separate task, resulting in redundant computation and enlarged complexity of the recognition algorithm, the multi-task paradigm makes use of shared representations across tasks, enabling the model to learn common designs and representations that are valuable for multiple tasks [1]. This method encourages the model to learn from its experiences with different tasks, thus enhancing its performance across the board. The model’s efficiency and efficacy are both improved by the shared learning across tasks, and more comprehensive comprehension of the visual input is provided, allowing for the modeling of complex interdependencies and linkages. Better generalization is another benefit of multi-task visual recognition since the model is trained to extract high-level features that are robust and meaningful across different recognition tasks [2]. Deep learning (DL) has been revolutionizing the visual recognition tasks (i.e., classification, detection, segmentation, colorization, etc.) in many application domains owing to the powerful learning capabilities that enable the DL models to automatically process and extract patterns of disease from plant images without the need for any hand- crafted features or engineered features [3,4]. Among the DL methods, convolutional neural Sustainability 2023, 15, 16791. https://doi.org/10.3390/su152416791 https://www.mdpi.com/journal/sustainability