Authors:
Rima Grati
1
;
Emna Abdallah
2
;
Khouloud Boukadi
2
and
Ahmed Smaoui
2
Affiliations:
1
Zayed University, Abu Dhabi, U.A.E.
;
2
Mir@cl Laboratory, Sfax University, Sfax, Tunisia
Keyword(s):
Smart Agriculture, Classification, Potato Leaf Disease, Transfer Learning, Explainable Artificial Intelligence.
Abstract:
Agricultural productivity is vital to global economic development and growth. When crops are affected by diseases, it adversely impacts a nation’s financial resources and agricultural output. Early detection of crop diseases can minimize losses for farmers and enhance production. Symptoms of diseases may take form in different parts of plants. However, the leaves, especially those of potatoes, are most commonly used in disease detection because they are buried deep in the ground. Deep learning-based CNN methods have become the standard for addressing most technical image identification and classification challenges. To improve training performance, the attention mechanism in deep learning helps the model concentrate on the informative data segments and extract the discriminative properties of inputs. This paper investigates spatial attention, which aims to highlight important local regions and extract more discriminative features. Moreover, the most popular CNN architectures, MobileN
etV2, DenseNet121, and InceptionV3, were applied to transfer learning for potato disease classification and then fine-tuned by the publicly available dataset of PlantVillage. The experiments reveal that the proposed Att-MobileNetV2 model performs better than other state-of-the-art methods. It achieves an identification F-measure of 98% on the test dataset, including images from Google. Finally, we utilized Grad-CAM++ in conjunction with the Att-MobileNetV2 method to provide an interpretable explanation of the model’s performance. This approach is particularly effective in localizing the predicted areas, clarifying how CNN-based models identify the disease, and ultimately helping farmers trust the model’s predictions.
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