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Agriculture is crucial for food security but is severely threatened by crop disease and climate variability and causes severe yield loss. As the population grows worldwide, quick and accurate disease detection is critical. Deep learning, in particular through transfer learning, offers promising solutions, but most are computationally costly and unsuitable for real-time use in low-resource settings. There is limited research on lightweight models like SqueezeNet with optimized training parameters. This suggests the need for an efficient, high-accuracy, and deployable model to facilitate timely detection of tomato leaf diseases under real-world agricultural settings. This study presents a deep learning model based on the SqueezeNet framework for the detection and classification of tomato leaf diseases. Various combinations of optimizers (SGDM, ADAM, RMSProp) and learning rates (0.0004, 0.004) were employed during both training and testing phases, resulting in six configurations per case. The SqueezeNet model achieved 99.91% and 99.86% accuracy for TMC class classification during testing and training, with ADAM learning at 0.0004. ADAM at 0.0004 had ideal recall (100%) for the TH class during testing, and SGDM had 99.65% recall for the TYLCV class at the same learning rate, proving the model’s usefulness. The proposed framework is robust, with F1-Scores of 99.42% in ADAM testing at 0.0004 and 99.38% in SGDM training at 0.0004 for the TYLCV class. The model’s low misclassification rate (0–0.21%) boosts confidence. The ability to demonstrate classification performance and the minimal computational requirements of the proposed SqueezeNet-based system enhance the latter’s feasibility for use in real-time agricultural environments that are resource-constrained. Its scalability and resilience make it an excellent choice for utilization in advanced disease monitoring systems for tomato leaf diseases, facilitating quick, accurate diagnosis at the field level to facilitate enhanced precision agriculture practices.

Tomato yellow leaf curl disease (TYLCD) and tomato leaf curl disease (TLCD) cause serious losses in tomato production, especially in tropical and sub-tropical regions. In 2014–2015, tomato samples with TYLCD/TLCD-like symptoms were collected from different provinces of Iraq and infection with tomato yellow leaf curl virus (TYLCV) identified. To study the diversity of TYLCV, DNA of eight positive samples from this survey was used for rolling-circle amplification, cloning and sequencing. Pairwise nucleotide sequence comparisons with complete genomes showed that the Iraqi TYLCV isolates belonged to the strains TYLCV-IL and TYLCV-Mld. In a phylogenetic analysis, the Iraqi TYLCV-IL isolates grouped into three distinct clades, consisting of TYLCV-IL (A) and the two new variants TYLCV-IL (D) and TYLCV-IL (E). The Iraqi isolate of TYLCV-Mld grouped into the newly proposed TYLCV-Mld (D) variant. For one sample, sequencing also revealed co-infection with tomato leaf curl Palampur virus (ToLCPalV). The phylogenetic tree of ToLCPalV DNA-A showed a close relationship between the isolates of different hosts from Iraq and Iran. No evidence of recombination was detected in ToLCPalV DNA-A, but recombination was observed for the TYLCV isolates. The results indicate that there is a high diversity of TYLCV in Iraq, including new variants, that is partly shared with Kuwait and countries in the Eastern Mediterranean Region. Occurrence of multiple TYLCV variants and ToLCPalV can act as a potential threat to tomato production in Iraq.

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

Tomato leaf diseases can incur significant financial damage by having adverse impacts on crops and, consequently, they are a major concern for tomato growers all over the world. The diseases may come in a variety of forms, caused by environmental stress and various pathogens. An automated approach to detect leaf disease from images would assist farmers to take effective control measures quickly and affordably. Therefore, the proposed study aims to analyze the effects of transformer-based approaches that aggregate different scales of attention on variants of features for the classification of tomato leaf diseases from image data. Four state-of-the-art transformer-based models, namely, External Attention Transformer (EANet), Multi-Axis Vision Transformer (MaxViT), Compact Convolutional Transformers (CCT), and Pyramid Vision Transformer (PVT), are trained and tested on a multiclass tomato disease dataset. The result analysis showcases that MaxViT comfortably outperforms the other three transformer models with 97% overall accuracy, as opposed to the 89% accuracy achieved by EANet, 91% by CCT, and 93% by PVT. MaxViT also achieves a smoother learning curve compared to the other transformers. Afterwards, we further verified the legitimacy of the results on another relatively smaller dataset. Overall, the exhaustive empirical analysis presented in the paper proves that the MaxViT architecture is the most effective transformer model to classify tomato leaf disease, providing the availability of powerful hardware to incorporate the model.

In natural environments, tomato leaf disease detection faces many challenges, such as variations in light conditions, overlapping disease symptoms, tiny size of lesion areas, and occlusion between leaves. Therefore, an improved tomato leaf disease detection method, DM-YOLO, based on the YOLOv9 algorithm, is proposed in this paper. Specifically, firstly, lightweight dynamic up-sampling DySample is incorporated into the feature fusion backbone network to enhance the ability to extract features of small lesions and suppress the interference from the background environment; secondly, the MPDIoU loss function is used to enhance the learning of the details of overlapping lesion margins in order to improve the accuracy of localizing overlapping lesion margins. The experimental results show that the precision (P) of this model increased by 2.2%, 1.7%, 2.3%, 2%, and 2.1%compared with those of multiple mainstream improved models, respectively. When evaluated based on the tomato leaf disease dataset, the precision (P) of the model was 92.5%, and the average precision (AP) and the mean average precision (mAP) were 95.1% and 86.4%, respectively, which were 3%, 1.7%, and 1.4% higher than the P, AP, and mAP of YOLOv9, the baseline model, respectively. The proposed detection method had good detection performance and detection potential, which will provide strong support for the development of smart agriculture and disease control.

Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

The human population is growing at a very rapid scale. With this progressive growth, it is extremely important to ensure that healthy food is available for the survival of the inhabitants of this planet. Also, the economy of developing countries is highly dependent on agricultural production. The overall economic balance gets affected if there is a variance in the demand and supply of food or agricultural products. Diseases in plants are a great threat to the yield of the crops thereby causing famines and economy slow down. Our present study focuses on applying machine learning model for classifying tomato disease image dataset to proactively take necessary steps to combat such agricultural crisis. In this work, the dataset is collected from publicly available plant–village dataset. The significant features are extracted from the dataset using the hybrid-principal component analysis–Whale optimization algorithm. Further the extracted data are fed into a deep neural network for classification of tomato diseases. The proposed model is then evaluated with the classical machine learning techniques to establish the superiority in terms of accuracy and loss rate metrics.

Tomatoes are one of the world’s greatest valuable vegetables and are regarded as the economic pillar of numerous countries. Nevertheless, these harvests remain susceptible to a variety of illnesses which can reduce and destroy the generation of healthy crops, making early and precise identification of these diseases critical. Therefore, in recent years, numerous studies have utilized deep learning (DL) models for automatic tomato leaf illness identification. However, many of these methods are based on a single DL architecture that needs a high computational ability to update these hyperparameters leading to a rise in the classification complexity. In addition, they extracted large dimensions from these networks which added to the classification complication. Therefore, this study proposes a pipeline for the automatic identification of tomato leaf diseases utilizing three compact convolutional neural networks (CNNs). It employs transfer learning to retrieve deep features out of the final fully connected layer of the CNNs for more condensed and high-level representation. Next, it merges features from the three CNNs to benefit from every CNN structure. Subsequently, it applies a hybrid feature selection approach to select and generate a comprehensive feature set of lower dimensions. Six classifiers are utilized in the tomato leaf illnesses identification procedure. The results indicate that the K-nearest neighbor and support vector machine have attained the highest accuracy of 99.92% and 99.90% using 22 and 24 features only. The experimental results of the proposed pipeline are also compared with previous research studies for tomato leaf diseases classification which verified its competing capacity.

Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.