Tomato Leaf Disease Detection

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.

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

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.

This study aims to detect leaf diseases in tomato plants by applying the Xception model in the Convolutional Neural Network (CNN) method. The study categorizes tomato conditions into three main categories: Early Blight, Late Blight, and Healthy. Early Blight is generally infected by specific pathogens that cause spots and damage in the early stages of plant growth, while Late Blight is infected by pathogens in the later stages of the growing season. Meanwhile, the healthy category indicates normal conditions without disease symptoms. The dataset used consists of 300 tomato images, with each category having 100 images. In the model training phase using the fit method in TensorFlow, 17 epochs were performed to teach the model to recognize patterns in tomato leaf disease images in the training dataset. The model testing results on 30 tomato leaf images showed an accuracy rate of 85.84%. This result indicates a positive indication that the developed CNN model performs well in detecting and classifying tomato leaf conditions. Thus, this research can contribute to improving the understanding and management of leaf diseases in tomato plants to support more productive and sustainable agriculture.

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.

The early and accurate detection of diseases in tomato plants is crucial for ensuring high crop yields and quality. Traditional methods of disease identification are often labor-intensive, time-consuming, and subject to human error. This research presents an enhanced classification system for detecting tomato leaf diseases using Convolutional Neural Networks (CNNs). Leveraging the power of deep learning, our approach significantly improves the accuracy and efficiency of disease identification compared to conventional techniques. We developed a CNN-based model that was trained and validated using a comprehensive dataset of tomato leaf images, encompassing various disease classes and healthy samples. The model architecture was meticulously designed to optimize feature extraction and classification performance. Extensive data augmentation techniques were employed to enhance the robustness and generalization capability of the model. Our enhanced classification system achieved remarkable accuracy, outperforming existing models in the literature. The results demonstrate the model's ability to effectively differentiate between multiple disease types and healthy leaves, even under challenging conditions such as varying lighting and background noise. This advancement holds great potential for integration into automated agricultural systems, providing farmers with a reliable tool for early disease detection and management.The implementation of this CNN-based improved classification system can lead to significant advancements in precision agriculture, minimizing crop losses and promoting sustainable farming practices. Future work will focus on extending this approach to other crops and refining the system for real-time field applications.

The present article is an automatic study based on deep learning to detect and categorize plant leaf diseases on Common rust in Corn, Early blight in Potato, and Bacterial spot in Tomato. The proposed model makes use of a Convolutional Neural Network (CNN) architecture toward the maximization of features extraction and classification rates. The proposed CNN model has three convolutional layers whose filter depths expound on each other with max-pooling layers in between and a fully connected dense layer with dropout to avoid overfitting the models. Multi-class classification is implemented in the last output layer where softmax activation is done. The data is extracted from kaggle and methods like resizing, normalization, and augmentation are used to improve generalization of the model. Model performance is shown to be exceptional as experimental results indicate an overall accuracy of 99 percent across the test dataset. The classification report shows precision, recall and F1-scores approximating 1.0 in all three categories with a low rate of misclassification being recorded only in Early blight in Potato subset. The practical effectiveness of the model is supported by the demonstration in the confusion matrix since the classes are practically separated by the model without any mistake.

The objective of this paper is to detect tomato leaf disease using Faster region-based convolutional neural network (R-CNN). The tomato leaf disease recognition system utilizes a dataset consisting of healthy tomato leaves and eight leaf diseases, including early blight, late blight, leaf mold, mosaic virus, septoria, spider mites, yellow leaf curl virus, and leaf miner. The dataset is obtained from various sources, such as Kaggle, Google Images, Bing Images, and Roboflow Universe. Pre-processing techniques, including collage, tile, static crop, and resize, are applied to prepare the dataset for training. Data augmentation methods, such as flipping, 90° rotation, exposure adjustment, and hue modification, are applied to enhance the model’s robustness and generalize its performance. Specifically, we implemented Faster R-CNN as part of Detectron2 using its base models and configurations. The results demonstrate that the X101-FPN base model for Faster R-CNN with the default configurations of Detectron2 is efficient and general enough to be applied to defect detection. This approach results in an average precision (AP) detection score of 87.01% for validation results.

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.

Identifying tomato leaf diseases under real field conditions using convolutional neural networks and a chatbot

Plant diseases pose significant economic challenges and may lead to ecological consequences. Although plant pathologists have a significant ability to diagnose plant diseases, rapid, accurate, and early diagnosis of plant diseases by intelligent systems could improve disease control and management. This study evaluates six efficient classification models (classifiers) based on deep learning to detect common tomato diseases by analyzing symptomatic patterns on leaves. Additionally, group learning techniques, including simple and weighted majority voting methods, were employed to enhance classification performance further. Six tomato leaf diseases, including Pseudomonas syringae pv. syringae bacterial spot, Phytophthora infestance late blight, Cladosporium fulvum leaf mold, Septoria lycopersici Septoria leaf spot, Corynespora cassiicola target spot, and Alternaria solani early blight, as well as healthy leaves, resulting in a total of seven classes, were utilized for the classification. Deep learning models, such as convolutional neural networks (CNNs), GoogleNet, ResNet-50, AlexNet, Inception v3, and MobileNet, were utilized, achieving classification accuracies of 65.8%, 84.9%, 93.4%, 89.4%, 93.4%, and 96%, respectively. Furthermore, applying the group learning approaches significantly improved the results, with simple majority voting achieving a classification accuracy of 99.5% and weighted majority voting achieving 100%. These findings highlight the effectiveness of the proposed deep ensemble learning models in accurately identifying and classifying tomato diseases, featuring their potential for practical applications in tomato disease diagnosis and management.

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.

Agriculture is the backbone of India and more than 50% of the population is dependent on it. With the increasing demand for food with the increase in population, it is the need of time that crops should be prevented against diseases. More than 1K acres of land with tomato diseases got affected in Pune only during this pandemic (2021). It could have been prevented by correct identification of the disease and then by corrective measures. This paper presents the experimental and comparative study of tomato leaf disease classification using various traditional machine learning algorithms like random forest (RF), support vector machines (SVM), naïve bayes (NB), and deep learning convolutional neural network (CNN) algorithm. In this study, it is perceived that CNN with a pre-trained Inception v3 model was able to detect and classify better than traditional methods with more than 95% accuracy.

Early blight, late blight, bacterial spot, leaf mold, spider mites, yellow leaf curl virus and Septoria leaf spot are the most common diseases of tomato plant which cause major loss to agriculture sector. Diagnosis and prognosis at early stage are prime challenges to save life of tomato crop. This research presents a state-of-the-art Double-Channel Convolution Neural Network (D-CNN) model for diagnoses of 8 different Classes of Tomato Plant Leaves. The proposed model integrates two separate Convolutional Neural Network (CNN) channels with similar number of Conv Layers and Parameters. One Channel processes the pre-processed Red-Green-Blue (RGB) data, whereas the second channel processes the region segmented data obtained by applying a Multi-Otsu Thresholding algorithm. We verified the proposed model on the Plant Village dataset which consists of more than 16,000 images of tomato plant leaves. The proposed model attains an overall accuracy of 94% after 200 iterations, comparatively 3.2% faster than Support Vector Machine (SVM), 2.2% faster than Probabilistic Neural Network (PNN), and 8% faster than Residual Neural Network (ResNet-50).

Late blight, caused by the oomycete Phytophthora infestans and early blight, caused by the fungus Alternaria solani, are among the most destructive diseases of potato crops in Brazil and worldwide. Considering the importance of the chemical control on integrated systems and the need for detailed knowledge of the performance of resistance inducers and fungicides targeting the sustainability of production, the present study aimed to:1-characterize and compare the preemptive, residual, curative, antisporulative action and rain resistance of fungicides under controlled conditions; 2-: evaluate, under field conditions, the control potential of fungicides and resistance inductors and their effects on the total and commercial yield of potato tubers. All fungicides tested provided a high pre-emptive action against late and early blight. The systemic fungicides or high tenacity ones provided control of both diseases until 12 days after application, while translaminar ones until 9 days after application. As for the curative and anti-sporulative action, the systemic fungicides stand out when applied until 24 hours postinoculation, while the translaminar ones inhibited late blight when applied until 12 hours postinoculation. The contact fungicides stand out concerning pre-emptive action only. Similarly, systemic, translaminar and high tenacity fungicides were less affected by the simulated rain. An increase in drying time promoted higher retention and absorption of the products, so decreasing the negative precipitation impact. The better levels of control, yield and tubers quality were reached with the use of the following fungicides in the control of late blight: mandipropamid+chlorothalonil, fluopicolide+propamocarb, dimetomorph+ametoctradin, mandipropamid, fenamidone+ propamocarb, bentiavalicarb+fluazinam, followed by dimetomorph+chlorothalonil, mefenoxam+ chlorothalonil and famoxadone+cymoxanil+mancozeb. In the control of early blight the most efficient were: azoxystrobin+difenoconazole, pycoxystrobin, pyraclostrobin+metconazole, trifloxystrobin+tebuconazole, azoxystrobin, boscalid+pyraclostrobin, iprodione+pyrimethanil and cyprodinil. Acibenzolar-s-methyl (ASM) reduced the severity of late and early blight, but promoted an increase in the tuber yield only in late blight affected field. Potassium phosphite (Pp) also reduced the severity of late blight, although it did not have influenced the yield. Except for mandipropamid, the addition of ASM to mefenoxam+mancozebe, cymoxanil+mancozeb or mancozeb led to a better control of late blight, but only when mixed with mancozeb it promoted increase in the yield. Addition of Pp to mandipropamid, mefenoxam+mancozeb, cymoxanil+mancozeb, mancozeb and ASM to azoxystrobin, difenoconazole did not result either in the control of late and early blight or in an increase of yield. Mancozeb and ASM did not differ as for severity, progress of late blight and increasing yield, but however, mancozeb+ASM had a superior efficiency than ASM. Addition of ASM to application programs reduced late and early blight and increased yield only when added to programs in which prevailed translaminar and contact fungicides. Pp did not influenced any of the tested programs against late blight.