Smart Agriculture in Morocco: An Intelligent Deep Learning Framework for Crop Disease Diagnosis

Abstract

The Moroccan agricultural sector is currently navigating a pivotal transformation driven by the “Generation Green 2020–2030” national strategy, which places a high priority on the digitalization of farming practices to bolster resilience against climate volatility and phytopathological risks. This study proposes a robust Smart Agriculture Framework engineered to automate crop disease diagnosis within mobile environments with limited resources. Unlike generic standard Deep Learning models often unsuited for local specificities, the methodology presented here is specifically tailored to Morocco’s agroecological context, targeting three strategic crops: Tomato (Souss-Massa region), Potato (Gharb plains), and Wheat (Chaouia region). A hybrid intelligent architecture is introduced that integrates a lightweight Convolutional Neural Network (CNN) with Particle Swarm Optimization (PSO-CNN) for autonomous hyperparameter tuning. The proposed framework was validated using a curated dataset of 15,000 images, rigorously augmented to reflect local field conditions, yielding a classification accuracy of 94.7%. This work effectively bridges the gap between theoretical AI architectures and practical Precision Farming, providing a rapid decision support system to minimize yield losses and align with the national objective of establishing a digitally empowered agricultural ecosystem.

Plant phenotyping relevance

植物画像から病害状態を推定するCNNベースの診断手法を開発し、画像データセットで検証しており、表現型取得・推定が中心である。

Code and data availability

The paper describes a curated 15,000-image Moroccan crop disease dataset and a PSO-CNN framework, but provides no public deposit, repository, or availability statement for the dataset, images, code, or trained model. All cited datasets (e.g., PlantVillage) are prior work, not this paper's assets.

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