Jul 2026· 2026 International Conference on Advanced Computing and Knowledge Engineering (ICACKE)· pp. 1-6· 0 citations· 25 references
Abstract
Precise identification of a disease in a crop is a critical step in protecting yields, optimizing inputs, and managing this sustainably. Decisions based on image-based deep learning models have achieved high diagnostic accuracy on controlled datasets, but in field variability, similar symptoms, nutrient stress and unfavorable environmental conditions, the decisions based on images could be uncertain. Study compares the performance of classical machine learning baselines, custom-trained convolutional neural networks (CNNs), and pretrained or transfer learning models on the challenging PlantVillage benchmark dataset with 54,305 RGB images of leaves across 38 crop-health classes and 14 plant species. The overall best classical methods was SVC using RBF kernel with high accuracy of 83.58% and F1 score of 82.96%. As depth and regularization increased, it was observed that the accuracy gradually increased and reached 95.71% for the functional CNN with dropout. The best results at the image level were obtained by the Transfer learning method: MobileNetV2 reached an accuracy of 99.30% and an F1-score of 99.30%. Based on these findings, the paper proposes an interpretable multimodal transfer learning decision-support architecture, comprising a MobileNetV2 image encoder, a residual MLP for soil attributes and dual LSTM encoders for a 48-hour and a 168-hour weather window. The evidence image, soil, full-weather and shortweather evidence are respectively assigned explicit weights of 0.80, 0.10, 0.05, and 0.05 in the proposed late-fusion mechanism. The framework is modular, explainable, and as desirable as possible in situations of decision support in the field where a diagnostic aspect is to be enhanced by the agronomic context.
Plant diseases have long been considered a major threat to global food production systems. Therefore, early diagnosis is vital to mitigate the risk of these diseases. This task can be challenging, as the number of harmful diseases is substantial. One technology that has gained widespread interest is artificial intelligence, specifically deep learning, which is used to identify plant diseases using leaf patterns. This paper presents two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture. Experiments were carried out using the PlantCity dataset, which consists of twelve subsets of diverse crop species representing fruits, vegetables, and grains with variations among the subsets, including the number of classes, the subset sizes, class distribution, and visual complexity of disease symptoms. The two models were evaluated using multiple metrics, including accuracy, loss, precision, recall, and F1-score. Explainable AI using the LIME technique was deployed to better interpret the acquired results. Results showed that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model, with accuracies ranging from 86% to 99% across eleven experimented crops. In order to perform an independent experimental validation for the developed model, future work will focus on constructing a local crop dataset captured from Iraqi fields to evaluate the developed models based on the local environment.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
This research presents the evaluation of plant leaf disease detection system based on a convolutional neural network (CNN) optimized with different nature-inspired algorithms, and indicates that application of the grey wolf optimizer (GWO) for tuning key hyperparameters of the model produced the best results.
This study presents a novel CNN for multi-class classification of 38 diseases, demonstrating an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
Experimental data show that the proposed EfficientNetB0V2 + CNN model achieves superior performance, with higher accuracy and better precision, recall and F1 Score across all classes, highlighting the effectiveness of the suggested approach in detecting complex disease patterns.
Nisha Rani· International journal of com...· 0 citations
The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.
W. Zalmi, Rahmi Putri Kurnia, Dyah Listianing Tyas· Informatik : Jurnal Ilmu Kom...· 0 citations
The proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.
Sonali Kamra, Vijay Laxmi· International Journal of Res...· 0 citations
