Jul 2026· AI Computer Science and Robotics Technology· 0 citations· 23 references
Abstract
Early detection of plant leaf diseases is beneficial as it helps agriculturists to apply remedial measures well in advance. This is highly recommended for a good yield from crops, which enhances the economy of an agriculture-based country. Computer vision and deep learning techniques, used in the field of precision agriculture, facilitate early detection and classification of plant leaf diseases. The literature proclaims that deep learning models outperform machine learning approaches for the classification of leaf diseases. In this paper, the state-of-the-art deep learning methods for detection and classification are applied on banana leaf dataset. The AlexNet, VGG19, DenseNet201, ResNet50, and MobileNetV2 convolutional neural networks are the models compared in this paper. The real-time images of banana leaves are used to train and test these models using Python programming. Healthy and two common diseases of banana leaves, namely Leafspot and Sigatoka, are classified in this work. After data augmentation and preprocessing, all the models could achieve good testing accuracies of more than 90.6% in the 80 of training, 10% of validation, and 10% of testing sets. ResNet50 deep technique outperforms the other architectures in 80% of the training set. The training and testing accuracies depend on the data augmentation and image pre-processing techniques.
Plant leaf diseases are known to affect agricultural productivity and food security on a global level. "Therefore, the detection and diagnosis of diseases are important aspects of maintaining the health of crops on a sustainable level. Traditionally, the detection of diseases in plants is performed manually by experts. This process is considered to be a tedious and time-consuming task due to the chances of human error during the process. To overcome the challenges of traditional methods of detecting and diagnosing diseases in plants, a deep learning-based system is proposed in this research study to detect and classify diseases in plants. This system uses the object detection model YOLOv8x and YOLOv10x to detect the objects in the images and classify the images accordingly. Deep learning models are used to classify the images of the plants. This study uses various deep learning models like the convolutional neural network model ResNet50 and EfficientNet, and the transformer model Vision Transformer and Swin Transformer. Moreover, a hybrid model is proposed in this study by combining the transformer and convolutional neural network model to improve the efficiency of the system in detecting and classifying the diseases of the plants. This system uses the PlantVillage dataset to classify the images of the plants and detect the diseases accordingly. From the results obtained in this study, it can be observed that the proposed system is highly efficient in detecting and classifying the diseases of the plants with the help of the transformer and hybrid model.
Plant leaf diseases are a major concern in agriculture because they reduce crop quality, lower productivity, and cause economic losses to farmers. Early detection of these diseases is important for protecting plants and improving overall crop management. However, manual identification through visual inspection is often slow and may produce inaccurate results, especially in the early stages of infection. To overcome this problem, this work proposes an automated leaf disease detection system using image processing and deep learning techniques. OpenCV is used for preprocessing the leaf images through resizing, noise removal, color normalization, and enhancement, while a Convolutional Neural Network (CNN) is trained to extract important features and classify the images into different disease categories. The dataset consists of healthy and diseased leaf images collected from publicly available sources and is divided into training, validation, and testing sets for effective model development. For practical implementation, the trained model is integrated into a Flask-based web application that allows users to upload leaf images and obtain predictions easily. The performance of the system is evaluated using accuracy, precision, recall, and F1-score, and the model achieved an accuracy of 95%. The results show that the proposed system can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T S, K. U, A. Jajur. J· World Journal of Advanced En...· 0 citations
This paper explores how deep learning methods can be used to monitor the health of crops and identify diseases, particularly for the apple crop. As the need for food security and sustainable farming methods increases, there is a strong demand for early detection of crop diseases. We have used a Convolutional Neural Network (CNN), based on the model of VGG16 architecture, since the model is known to be effective in image classification. The dataset contained 7771 training images for to enhance machines deep learning regarding plant diseases. Following that, validation image collection of 1747 images divided into four health conditions of the apple crops. In addition, the model was evaluated using 196 new images as a final test. To enhance the model capacity for recognizing diseases of real leaves rather than just memorize exact training pictures, data augmentation was used with ImageDataGenerator of TensorFlow. This means the training images were zoomed, rotated, and shifted to enable the machine detects more variations. Ten epochs of training were performed to measure the model accuracy. The results indicated that the model obtained significant improvement in training and validation accuracy from 56.43% to 78.12% and 92.94 to 96.93%, respectively. Most impressively, the final test dataset, which contained completely new images, scored an accuracy rate of 98%. The results indicate that in the architecture field the application of deep learning methodologies is effective, suggesting that automated detection of diseases by using sophisticated image analysis manages crop diseases identification efficiently. Combination of these methods successfully creates avenues for novel research to built real-time systems of crop disease monitoring, which help farmers increase their productions and farm their lands sustainably.
Rondik J. Hassan, Kazheen Ismael Taher· International journal of com...· 0 citations
Plant diseases greatly affect agricultural production, especially in developing countries, where prompt diagnosis can be quite challenging due to the limited availability of experts in real-time. Deep learning techniques for image analysis is gaining popularity and are increasingly considered an alternative to traditional manual inspection of plants. This research presents the evaluation of plant leaf disease detection system based on a convolutional neural network (CNN) optimized with different nature-inspired algorithms. The backbone model is based on the EfficientNet-B0 pretrained on ImageNet. Therefore, transfer learning is used to adapt the model to an updated PlantVillage dataset. Experiments have been conducted with multiple nature-inspired algorithms to improve generalisation and training efficiency of the prediction model. Different data preparation techniques have been carefully applied to the dataset, creating a unified approach to ensure consistency in the preprocessing pipeline for the training, validation, and testing phases. Our experiments indicate that application of the grey wolf optimizer (GWO) for tuning key hyperparameters of the model, including dropout, learning rates, and weight decay produced the best results, with and accuracy around 99.45%.
Strawberry is highly prone to numerous foliar diseases which can drastically decrease the yield and quality unless well identified. The conventional ways of disease identification are based on manual inspection, which is very tedious and ineffective in large farms. The creation of artificial intelligence (AI) has enabled the application of deep learning (DL) models to identify illnesses in plants through the examination of images automatically. This paper will give a detailed comparison and analysis of various deep learning models used to diagnose and classify strawberry leaf diseases. A collection of images of normal and diseased strawberry leaves is used with a number of more advanced convolutional neural network (CNN) models, such as VGG16, ResNet50, and InceptionV3, and transfer learning is used to use the knowledge gained with pre-trained models and simplify the training process. The simulated results indicate that each of the models is a reasonable classification model, with InceptionV3 being the most accurate and VGG16 being the best tradeoff between accuracy and resource-efficiency, so it is applicable in real-time and resource-constrained conditions.
S. R· 2026 4th International Confe...· 0 citations
Early and correct diagnosis of the crop leaf diseases is essential to guarantee agricultural output, reduce yield damages and to sustain agriculture. Traditional diagnostic techniques or disease diagnostics requiring manual examination are manual, subjective and cannot be applied in large-scale or real-time agricultural monitoring. Despite the recent progress in the field of deep learning, which has shown that high classification rates can be achieved with the help of deep learning in the field of plant disease identification, most of the present methods are only able to perform image-level classification, are not able to localize the disease, and cannot be deployed in real-time in the field. This study introduces a deep learning system of real-time detection and classification of crop leaf diseases that combines effective object detection and disease classification in a single system. The strategy proposed uses a one-stage detection model and an optimized convolutional backbone, data augmentation, and transfer learning to balance the accuracy, robustness, and computational efficiency with the proposed strategy. With standard performance metrics and real-time inference analysis the framework is tested on a curated dataset of about 6,500 samples of crop leaf images of five representative classes including healthy and diseased ones. Experimental data indicate good and consistent performance in terms of disease-wise and a false positive rate of 95.6 and F1-score of 95.4 respectively. The normalized confusion which is depicted in the normalized confusion matrix is highly dominant on the diagonal meaning that there is no inter-class confusion and the sensitivity is certain in all the categories of the disease. The presence of the correct localization of the symptomatic area of the leaves in various visual conditions with the help of qualitative detection is proved. The unified detection classification design proves to be effective as verified by comparative and ablation studies, and real-time assessment demonstrates an inference rate of 26.3 FPS, which is appropriate to be used in edge-based and in-field implementation. All in all the proposed framework will help in closing the gap between laboratory models of high accuracy and deployable real-time agricultural solutions. The method allows localizing the disease and diagnosing it within a short time, which contributes to the development of sustainable and precision farming systems, facilitates early intervention, specific treatment, minimizes the use of chemicals, and enhances crop management.
S. Phani Praveen, Karuna Arava, Tenali Nagamani et al.· International Journal of Adv...· 0 citations