REAL-TIME TOMATO LEAF DISEASE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Tomato leaf diseases significantly affect crop productivity, and manual inspection often leads to misclassification due to the visual similarity of symptoms. Recent studies have shown that Convolutional Neural Networks (CNN) provide high accuracy in leaf–based plant disease classification across various plant species, highlighting their potential for early disease detection. This study aims to develop an accurate tomato leaf disease classification system using a CNN model trained on the Kaggle tomato leaf dataset consisting of four classes: Leaf Blight, Bacterial Spot, Leaf Scab, and Healthy. The methodology includes literature review, dataset acquisition, preprocessing, augmentation, CNN architecture design, model training, and performance evaluation. Preprocessing techniques such as resizing and normalization were applied, followed by augmentation using random flipping and rotation to increase dataset variability. The proposed model was trained for 40 epochs with a batch size of 16. Results show consistent accuracy improvement, reaching 0.98 training accuracy with a loss of 0.07, while validation accuracy peaked at 0.94. Testing on both single and multiple images demonstrates strong prediction confidence, with minor misclassifications in visually similar cases. Overall, the system effectively identifies tomato leaf diseases and reinforces the suitability of CNN for supporting early plant disease detection in smart agriculture applications.

Plant diseases cause to decreases in product in agriculture. The majority of farmers find it challenging to identify and control plant diseases. It is necessary to spot diseases early in order to minimize future losses. In this paper, a deep learning method utilizing convolutional neural networks (CNNs) to identify tomato leaf diseases is described. For example, to classify tomato leaf images into one of ten categories—healthy, yellow leaf curl virus (YLCV), bacterial spot (BS), early blight (EB), leaf mold (LM), spectoria leaf spot (SLS), target spot (TS), two spotted spider mite spot (TSSMS), mosaic virus (MV), and late blight (LB)—the proposed method initially prepares the images of the leaves. A dataset which include 16021 images of tomato leaves was used to train the model. After 10 epochs, 20 epochs, and 50 epochs of training, the accuracy was 64%, 94%, and 97%, in that order. The results provided show that the proposed approach has been effective for determining tomato leaf diseases, and that its performance gets improve with time. The early detection and prevention of tomato diseases by the use of an automated method has the capacity to boost tomato crop quality and output. The In this review paper we are going to show the different approaches that can be used to deal with the classification of tomato leaf diseases.

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

This composition bargains with the Tomato leaf infection discovery and classification utilizing different strategies like Convolutional Neural Network (CNN), Regions with CNN (R-CNN), Fast R-CNN and Faster R-CNN. The main issue in the agricultural sector is leaf diseases, which have an impact on crop yield and financial gain. Early detection of leaf diseases in plants is essential to prevent losses to the agricultural sector. There are several tomato leaf diseases that affect the crop's leaves, including the Mosaic virus, Early Blight, Healthy, Septoria leaf, and Bacterial spot. Using deep learning algorithms and image processing methods, we can identify the diseases in tomato leaves using developing deep learning approaches. The implementation procedures in our proposed work involves data collection, pre-processing, training, feature extraction, testing, and classification utilising the Visual Geometry Group (VGG 16) to identify damaged or healthy leaves. VGG 16 is incorporated to categorise the leaves as healthy or diseased based on the data and Regression's boundary box method is adopted. Therefore, using Faster RCNN, a model is created to identify and categorise diseases from every image of a tomato leaf that is used as an input, providing a forecast with a considerably greater degree of accuracy. We obtain an accuracy of approximately 98% after fitting the collected features into the neural network over 20 iterations.

Plant diseases that can destructively harm the agriculture are usually spotted with bare eyes, and this may consume a longer time leaving a chance of incorrect detection. Early detection can solve this problem and reduces the risk of decreasing plant production. The central focus of this study is to automatically detect tomato plant leaf diseases in a faster way by implementing Deep Learning (DL), which can be effectively applied for image classification using different convolutional neural network (CNN) architectures. This paper offers to classify images via transfer learning architectures including VGG-16, VGG-19, and Inception-V3 models with CNN to identify diseases. Applying these models on a dataset containing 11000 images of tomato leaves including signs of 9 different diseases and healthy leaf, high accuracy is gained. Finally, the comparative analysis done on the three pre-trained models within this paper summarizes that the best-suited model for detecting tomato leaf diseases with the best accuracy is Inception-V3. In order to support the claim, necessary results and data are also included in this paper.

Plant diseases are a severe threat to the environment, economy, and health. Early disease identification remains a challenging task in Iraq due to the scarce of the necessary resources and infrastructure. This paper uses various deep learning algorithms to detect different diseases on plant leaves and detect healthy ones, using an RGB camera as a crucial part of our real-time autonomous greenhouses' robot along with using two datasets, plant-village and cotton dataset, to investigate the best convolutional neural network architecture. The first dataset contained 10,190 images from the plant-village open datasets; it includes four crops with ten distinct classes of diseased and healthy leaves. Moreover, the cotton dataset contained 2,204 images for training and 106 images for testing; it has four classes of diseased and healthy plants and leaves. Different network architectures were tested in this paper for the best suitable lightweight architecture for our mobile robot. Results show that the best performance is 99.908% which achieved by the VGG16 network. The highest accuracy of VGG16 obtained in our research makes it the best tool for our autonomous plant disease detection robot. Index Terms— Convolutional neural networks architectures, Computer vision, Deep learning, Leaf disease detection and classification, Precision agriculture.

Tomato plant diseases caused by microorganisms such as fungus, bacterial etc. have been threatening the quality and quantity of tomato production for a few centuries. Therefore, precaution is necessary during the early stage to detect and cure the diseases that occurred on the leaves before the diseases spread to result in plant death. However, the detection method employed currently is inconsistent and inefficient. Most farmers employ manual visualisations to identify the types of plant diseases as the automatic monitoring and classification of diseases cannot be achieved. Such obstacles drive the need for a plant disease detection system, aided by artificial intelligence (AI). Therefore, in this work, four different Convolutional Neural Network (CNN) models for tomato plant diseases detection, namely VGG-16, ResNet-50, DenseNet-121 and ResNet-101 were implemented and the hyperparameters were manipulated for parametric studies. The performance of the different architectures with the selected hyperparameters was evaluated and compared according to the well-established evaluation metrics for deep learning; Precision, Recall, F1 score, Matthews correlation coefficient (MCC) and Accuracy. Combining all its merits, the ResNet-50 model which had achieved an accuracy of 100% with a shorter training time was proposed as the final model to be implemented in the tomato plant diseases classification system. In short, a CNN model that is capable of effective classification of tomato leaf diseases has been developed and its benefits for tomato cultivation are much anticipated.

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.

Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co‐occurrence matrix (GLCM) and scale‐invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state‐of‐the‐art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.

Identifying and detecting a plant disease is a primary challenge in the farming segment. Specifically, for tomato farming: Early Blight, Late Blight, Mosaic Virus, Target Spot, Yellow Leaf Curl Virus (YLCV), Bacterial Spot, Leaf mold, Septoria leaf spot and two-spotted spider mite are nine general diseases that severely affect the yield. In this work, we propose convolutional neural networks (CNNs) based deep learning (DL) model for the identification of tomato leaf diseases. In our study, we take the Tomato Leaf Disease Dataset (TLDD) consisting of 18,160 images of the infected and the healthy tomato leaves from PlantVillage. We first select the most suitable and accurate deep learning models for disease identification experiments. Four popular models viz. SqueezeNet, ResNet50, InceptionV3 and DenseNet are chosen for the analysis. Lastly, using ImageAI library on Google Colaboratory (Colab), we train all the four models on the collected tomato-leaf-image dataset to identify the presence of above mentioned nine common tomato leaf diseases. Results from our experiments on the comparative study of the selected deep learning models identify nine different tomato leaf diseases. We infer that the InceptionV3 model provides the highest accuracy of 99.64%. Our chosen model provides faster detection with higher accuracy as compared to the other models. In future work, we intend to modify the algorithm to develop our own model for disease identification for other crops as well.KeywordsDeep learningConvolutional neural networkTomato leaf diseaseImageAI

In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their generalization. An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs with an excessive RF tend to over-fit the training data and fail to generalize to unseen testing data. As state-of-the-art CNN architectures - in computer vision and other domains - tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks. We study well-known CNN architectures and how their building blocks affect their receptive field. We propose several systematic approaches to control the RF of CNNs and systematically test the resulting architectures on different audio classification and tagging tasks and datasets. The experiments show that regularizing the RF of CNNs using our proposed approaches can drastically improve the generalization of models, out-performing complex architectures and pre-trained models on larger datasets. The proposed CNNs achieve state-of-the-art results in multiple tasks, from acoustic scene classification to emotion and theme detection in music to instrument recognition, as demonstrated by top ranks in several pertinent challenges (DCASE, MediaEval).

Every year, new Convolutional Neural Network (CNN) architectures appear to deal with different problems in the activity of image and video recognition. These architectures usually work along the ImageNet dataset for looking for the best performance of the CNNs without taking into account the video task where they are used. This can represent a problem if the task is Human Action Recognition (HAR) in video, since the CNN architectures are pre-trained with an image dataset that can practically contain any object, while HAR problem requires consecutive frames of people doing actions. To prove the idea that using CNNs pre-trained on an image dataset does not always achieve the best performance on a video dataset and that, therefore, it is worth comparing the performance of different CNNs under similar circumstances for the HAR problem, this work proposes an analysis between eight different CNN architectures. Each one of the CNN was exclusively trained with RGB images, which were extracted from the frames of the different classes of videos of HMDB51 dataset. To make the classification of an activity in video, we average the predictions taking into account the successes. We also made some ensembles with the best performance CNNs to measure the improvement in accuracy. Our results suggest that Xception is a strong baseline model that could be used by the community to make their comparisons of their proposals more robust.

Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology. Nevertheless, the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge. This approach is time-consuming, and high computational resources are mandatory. In this research, we propose a novel method based on Mutant Particle swarm optimization (MUT-PSO) Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification. Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network (MUTPSO-CNN) can find optimum CNN architecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy, precision/recall, and execution time.

In this paper, the Convolutional Neural Network (CNN) architecture, which is one of the deep learning architectures, is used to classify the basic circuit components drawn by hand. During the training and testing stages of the model, a new dataset containing images of 863 circuit components manually drawn by different people is created. The data set contains images of four different classes of circuit components such as resistor, inductor, capacitor and voltage source. All images have been fixed to the same size and converted to grayscale to increase recognition performance and reduce process complexity. In the study, training for four classes is performed with CNN architecture. Based on the CNN architecture, four new CNN models are employed with different the number of layers. The training and validation results of these models are compared separately, the model with the highest training and validation performance is observed with four layer CNN model (CNN-4). This model obtained 84.41% accuracy rate at classification task.

Plant diseases cause various pathogens, including bacteria, viruses, fungi, and protozoa. Plant diseases and pathogens have an impact on the entire crop and field area. Plant diseases can be identified by various techniques; however, traditional techniques are time-consuming and require more effort. Up-to-date machine learning based approaches are used to identify plant diseases based on the image data. This study investigated the application of convolutional neural network (CNN) models for the detection and classification of diseases affecting green leafy vegetables. Several state-of-the-art CNN architectures, including InceptionV3 and DenseNet121, were evaluated on an image dataset of diseased and healthy leaf samples. Preprocessing techniques such as scaling, cropping, grayscale conversion, and normalization were applied to enhance the input images. The CNN models demonstrated high diagnostic accuracy, with InceptionV3 and DenseNet121 exhibiting exceptional performance across multiple metrics like sensitivity, specificity, accuracy, recall, F-measure, and Matthews correlation coefficient (MCC).