The profitability and production capacity of farmers is eventually impacted by the substantial influence that plant diseases have on agricultural yields. Computer-aided detection techniques are essential for accurately identifying plant diseases [11]. To assist agricultural specialists, this study focuses on diagnosing illnesses of Turmeric Plant Disease. To compile this dataset, we painstakingly gathered photos from Turmeric plantations, recording a range of Turmeric Plant Disease signs in diverse climates. Following data collection, we organized and cleaned the photos to improve their quality for analysis as part of the data preparation process. We used thorough preprocessing methods to increase the dataset's training appropriateness. Data augmentation techniques like flipping, rotation, and brightness modifications were applied to add more unpredictability to the dataset. After that, the dataset was split into two subsets: 20% for testing and 80% for training. The training subset was used to train deep learning models, and their performance was assessed using the test data. This procedure made sure that there were no noise or irregularities in the dataset. To establish a well-structured basis for building and evaluating models for precise Turmeric Plant Disease diagnosis, we lastly divided the dataset into training and testing sets. Additionally, the dataset includes classifications such as Leaf Blotch Disease, Root Disease, Healthy Roots, Dry Leaf, and Healthy Leaf, as shown in Fig. 2. By outlining the systematic process for identifying turmeric plant diseases, this dataset serves as a visual guide, fostering advancements in agricultural disease management and supporting crop health monitoring initiatives.

We can vouch for the exceptional camera capabilities of the iPhone 14 Plus, which I used to take the photos. With a broad 120-degree field of view, the ultra-wide lens is perfect for photographing huge landscapes. Even in low light, crisp, clear images are guaranteed thanks to the 12 MP primary sensor and f/1.5 aperture. Deep Fusion and Night mode improve every picture in any kind of lighting. High contrast and brilliant colors are produced by the Photonic Engine. Every picture is a true depiction of quality and originality thanks to the front-facing 12 MP camera with Night mode, which also makes beautiful selfies.

Following a few crucial procedures shown in Fig. 3, the raw photos gathered from the turmeric fields were treated during the pre-processing phase to get them ready for deep learning model training [12]. Before training, only high-quality samples were left after data cleaning was completed to eliminate noisy, unnecessary, or duplicate images. The photos were then downsized to standardize all of them to 800 × 800pixel resolution to preserve uniformity throughout the collection. Following data labeling, the photos were divided into five primary categories: dry leaf, root disease, healthy roots leaf blotch disease, and healthy leaf. Several data augmentations [13] methods were used to improve the dataset's resilience, including image shifting, flipping, rotation, zooming, adding noise, and altering brightness. By creating new training samples, these augmentations broadened the dataset's variety and decreased the possibility of overfitting when training the model. Above all, the augmentation strategies were essential for enhancing the deep learning model's generalization and resilience, guaranteeing its performance in real-world scenarios where image differences may arise. All things considered, the dataset was completely optimized by this pre-processing pipeline for further model training and assessment.

By generating new data samples from preexisting ones, data augmentation significantly improves model optimization and generalizability. When working with tiny datasets, this method efficiently expands the amount of training data that is accessible without requiring the gathering of extra data. All things considered, data augmentation is essential to raising the effectiveness and dependability of machine learning models in a variety of fields [14]. Data augmentation greatly improves deep learning models, especially those that are employed for visual object recognition tasks. Adding a variety of picture modifications to the training dataset enhances the model's generalization abilities and helps avoid overfitting. A variety of augmentation techniques were used in this study, such as flipping, rotating, zooming, shifting, adding noise, sharing, and brightness adjustment. By introducing diversity into the dataset, these techniques improve the model's capacity to classify turmeric plant illnesses in various circumstances, including changes in leaf orientation, lighting, and angles, and allow it to generalize effectively to unknown samples.

Table 3 provides a summary of the particular augmentation methods used, together with information on their ranges and characteristics. By applying these augmentations to the training dataset, the variety of the samples was greatly increased, which improved model performance.

Augmentation plays a critical role in improving the robustness and generalizability of a model. To achieve this, we applied several techniques that helped the model better adapt to various visual conditions encountered in real-world situations. Fig. 4 demonstrates the results of applying these techniques to images from different disease categories

Flipping, rotating, zooming, shifting, noise addition, shearing, and brightening are all applied to the original photos by the augmentation pipeline for various turmeric plant situations, including leaf blotch, dry leaves, rhizome roots, healthy roots, and healthy leaves, as seen in Fig. 4. These enhanced photos give a better understanding of the diversity that was included to make sure the model is capable of identifying and categorizing plant diseases in a variety of difficult situations. This augmentation technique is essential since it improves the model's accuracy and dependability in practical applications.

The Inception-v3 model, a variant of the Inception network, is well known for its effectiveness and precision in picture classification tasks [15]. To categorize turmeric plant illnesses, we carefully assessed the Inception-v3 architecture in this study. Images from four different classes, each of which represents a certain state of the turmeric plant, make up the dataset. We set aside 80% of the data for training and 20% for validation. We used data augmentation approaches to improve the training process, which broadened the training data's variety and enhanced the model's ability to generalize to new situations. A batch size of 32 was used to train the Inception-v3 model across 20 epochs. The learning rate was dynamically modified using a learning rate scheduler during training. The models' effectiveness was evaluated using a range of metrics, such as accuracy, precision, recall, and F1 score (Eqs. (1) to (4)) [16]. These metrics offer a comprehensive assessment of the models' performance and shed information on their multifaceted classification abilities. We also monitored the training process using training and validation datasets. The training metrics were visualized and analyzed using TensorBoard.

A confusion matrix is a matrix that shows the performance of a machine learning model on a collection of test data [17]. In our investigation, we used the confusion matrix to assess how well our model classified lemon leaf disease. The ground truth labels for each sickness category are shown graphically in the matrix along with the model's predictions.

The confusion matrix (Fig. 5) shows the model's high accuracy of 97.36% with augmentation, correctly classifying most classes like Dry Leaf and Healthy Leaf, with minor misclassifications between Rhizome Disease Roots and Rhizome Healthy Roots. In contrast, the confusion matrix (Fig. 6) shows the model's performance without augmentation, achieving 95.71% accuracy, with similar misclassifications but still strong overall performance.

Using the Inception-v3 model with augmentation, the classification achieved an accuracy of 97.36%, highlighting the model's strong performance and its potential for accurately diagnosing turmeric plant diseases across various agricultural conditions. Without augmentation, the model achieved an accuracy of 95.71%, still demonstrating solid reliability and effectiveness in disease classification. The classification reports with and without augmentation are provided in Table 4, Table 5, respectively, offering detailed metrics for each scenario (Fig. 7, Fig. 8).

This study provides a comprehensive analysis of turmeric plant disease classification using advanced deep learning techniques. The Inception-v3 model was employed to identify various conditions affecting turmeric plants, utilizing a well-curated dataset. The results showcase the model's impressive performance, achieving an accuracy of 97.36% with augmentation. Its ability to accurately distinguish between different turmeric plant diseases demonstrates strong potential for real-world agricultural applications, facilitating early detection and effective prevention. Furthermore, the study's evaluation of performance metrics and the use of data augmentation techniques offer valuable insights for enhancing the model's effectiveness and interpretability.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.