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Crop diseases constitute a danger to worldwide food stability, diminishing harvest yield, grade, and total farming efficiency. Conventionally, the identification of plant ailments has depended on lengthy visual assessments by specialists, a method that frequently results in mistakes. Machine learning (ML) and artificial intelligence (AI), in particular Vision Transformers (ViTs) and Convolutional Neural Networks, deliver a swifter, automated substitute for recognizing plant illnesses through leaf image analysis. However, these frameworks are regularly faulted for their "black box" character, which curbs confidence in their outputs due to a lack of clarity. Our outcomes indicate that integrating Explainable AI (XAI) approaches like Grad-CAM, Integrated Gradients, and LIME notably boosts model explainability, rendering it simpler for users to detect the fundamental indicators of plant sicknesses. This investigation not only adds to the area of plant disease recognition but also supplies an original viewpoint on advancing AI openness in actual farming settings through the application of XAI methods. With training accuracy rates of 100.00% for ViT, 96.88% for EfficientNetB7, 93.75% for EfficientNetB0, and 87.50% for ResNet50, and equivalent validation accuracy rates of 96.39% for ViT, 86.98% for EfficientNetB7, and 82.00% for EfficientNetB0, our suggested architectures exceed earlier studies performed on the identical dataset. This illustrates a considerable leap in model capability while upholding clarity and dependability by way of explainable and trustworthy decision procedures.

The Nong Han Chaloem Phrakiat Lotus Park functions as a visitor destination and an educational hub centered on lotus plants. Nevertheless, its role as a training ground suffers from a lack of engagement and learning drive, a consequence of the outdated way lotus plant data is displayed. This investigation deployed the smart learning paradigm in the locale to amplify curiosity and motivation for education. To build a mobile application capable of displaying details for each type, convolutional neural networks (CNNs) were used to categorize lotus plant species. The boundaries of the work were defined by sorting 11 lotus plant species using a bespoke CNN model that relied on distinct methods (augmentation, dropout, and L2) and hyperparameters (dropout and the number of epochs). The goal was to secure a high-efficacy CNN model with a lower total parameter count than using three separate pre-trained CNN models (Inception V3, VGG16, and VGG19) as reference points. Model effectiveness was measured using accuracy, F1-score, precision, and recall. The outcomes showed that the CNN model integrating augmentation, dropout, and L2 regularization at a dropout rate of 0.4 and an epoch count of 30 achieved the highest test accuracy of 0.9954. The proposed superior model proved more precise than the pre-trained CNN benchmarks, with a pronounced improvement over Inception V3. In addition, the overall parameter count was cut down by a factor of about 1.80–2.19. These revelations showed that the proposed model, with its modest parameter count, achieved an acceptable level of classification precision.