Maize leaf disease recognition based on improved MSRCR and OSCRNet

Detecting pests and diseases on maize leaves is challenging. This is especially true under complex conditions, such as variable lighting and occlusion. Current methods suffer from low detection accuracy. They also lack sufficient real-time performance. Hence, this study introduces the lightweight detection method YOLOv11-RCDWD based on an improved YOLOv11 model. The proposed approach enhances the YOLOv11 model by incorporating the RepLKNet module as the backbone, which significantly enhances the model’s capacity to capture characteristics of maize leaf pests and diseases. Additionally, the CBAM is embedded within the neck feature extraction network to further refine the feature representation to augment the model’s capability to identify and select essential features by introducing attention mechanisms in both the channel and spatial dimensions, thereby improving the accuracy of feature expression. We have also improved the model by incorporating the DynamicHead module, WIoU loss function, and DynamicATSS label assignment strategy, which collectively enhance detection accuracy, efficiency, and robustness through optimized attention mechanisms, better handling of low-quality samples, and dynamic sample selection during training. The experimental findings indicate that the improved YOLOv11-RCDWD model effectively detected pests and diseases on maize leaves. The precision reached 92.6%, while the recall was 85.4%. The F1 score was 88.9%, and the mAP@0.5 and mAP@0.5~0.95 demonstrated an improvement of 4.9% and 9.0% over the baseline YOLOv11s. Notably, the YOLOv11-RCDWD model significantly outperformed other architectures such as Faster R-CNN, SSD, and various models within the YOLO series, demonstrating superior capabilities in terms of detection speed, parameter count, computational efficiency, and memory utilization. This model achieves an optimal balance between detection performance and resource efficiency. Overall, the improved YOLOv11-RCDWD model significantly reduces detection time and memory usage while maintaining high detection accuracy, supporting the automated detection of maize pests and diseases, and offering a robust solution for intelligent monitoring of agricultural pests.

Maize, a vital global food crop, is susceptible to various diseases impacting agricultural yield and quality, necessitating effective monitoring strategies. This study presents a comprehensive approach utilizing advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classification. Leveraging datasets from Kaggle, Harvard Dataverse, and Mendeley, the proposed CNN maize disease detection (MDD) system integrates optimization techniques, data augmentation, batch size adjustments, and fine-tuning strategies. Evaluation through data splitting reveals significant correlations among maize leaf classifications and the influence of data augmentation on image quality. Following training, the CNN-MDD model is deployed on a Microcontroller Unit (MCU) simulation, achieving promising optimization parameters: latency of 7.60 ms, RAM size of 726.60 KB, classifier flash size of 344.70 KB, and an accuracy of 94.60 %. These optimized parameters align well with MCU specifications, highlighting the potential for practical deployment on low-cost edge devices like Arduino Ble 33 Sense. Additionally, this research contributes to the creation of a Ghanaian maize crop pest and disease dataset and the development of an optimal and cost-effective CNN-MDD MCU edge device, aiming to provide local farmers in Ghana and Sub-Saharan Africa with a swift, offline solution for maize disease detection, thus fostering sustainable agricultural practices. We conduct a comparative analysis of Edge Impulse and TensorFlow, two prominent machine-learning platforms for TinyML model development and deployment in various applications, including smart crop disease detection. Utilizing a dataset comprising 12,344 raw images and 7,454 augmented images of healthy and diseased maize leaves, we evaluate the workflow, features, performance, and usability of each platform. Our findings indicate that Edge Impulse holds a slight advantage over TensorFlow in terms of ease of use, reliability, and memory footprint, whereas TensorFlow offers greater flexibility, customization options, and higher accuracy. We conclude that while both platforms are suitable for crop disease detection, a combined approach allows for leveraging TensorFlow's robust training capabilities for complex neural networks alongside the simplicity of Edge Impulse for deploying models on low-powered edge devices. This synergistic approach enhances the effectiveness and efficiency of crop disease detection systems, ultimately benefiting agricultural practices and food security.

Plant disease management is an essential process to minimize loss in the field of agriculture. Plant leaf disease detection(PLDD) technology helps the formers to reduce the loss of quality yield production. This study focuses on detecting paddy and maize plant leaf disease-detecting methods. It contributes to a PLDD method to improve detection accuracy and overall performance. The PLDD method introduced a whales-optimized artificial neural network (WOANN) method for classifying the four Maize and four rice leaf disease-related classes. The WOANN uses the whale optimizer's food-searching functionalities to improve the performance and detection accuracy of the dense net model. This WOANN classifies the maize leaf light, maize grey leaf spot, maize common rust, rice bacterial leaf blight, rice bacterial leaf steak, rice brown spot, and healthy leaves of both Maize and paddy. It uses the Hilbert-Schmidt independent criterion lasso correlation algorithm to support the WOANN classifier in selecting the significant features. The performance analysis shows that the WOANN-based approach achieves a maximum of 99.35% detection accuracy for maize leaf disease and 99.13% for paddy leaf disease. Its efficiency analysis shows that the WOANN-based approaches achieve a maximum accuracy rate than comparison approaches.

Trichoderma harzianum is well known to exhibit induced systemic resistance (ISR) to Curvularia leaf spot. We previously reported that a C6 zinc finger protein (Thc6) is responsible for a major contribution to the ISR to the leaf disease, but the types of effectors and the signals mediated by Thc6 from Trichoderma are unclear. In this work, we demonstrated that two hydrolases, Thph1 and Thph2, from T. harzianum were regulated by Thc6. Furthermore, an electrophoretic mobility shift assay (EMSA) study revealed that Thc6 regulated mRNA expression by binding to GGCTAA and GGCTAAA in the promoters of the Thph1 and Thph2 genes, respectively. Moreover, the Thph1 and Thph2 proteins triggered the transient production of reactive oxygen species (ROS) and elevated the free cytosolic calcium levels in maize leaf. Furthermore, the genes related to the jasmonate/ethylene signaling pathway were up-regulated in the wild-type maize strain. However, the ΔThph1- or ΔThph2-deletion mutants could not activate the immune defense-related genes in maize to protect against leaf disease. Therefore, we conclude that functional Thph1 and Thph2 may be required in T. harzianum to activate ISR in maize.

Rhizoctonia solani f. sp. sasakii (RSS) is a highly devastating soil-borne fungal pathogen inciting banded leaf and sheath blight (BLSB) disease in maize. In India, annually one percent of the total maize yield is reduced by BLSB. Due to continuous occurrence and wide spread of disease, the present experiment was conducted to assess the pathogenic variability among different isolates obtained from multi-geographical locations of India. Pathogenic behaviour of each isolates was predicted through the comparable study of their incubation period, average disease score and disease intensity on the three maize hybrids (Normal maize- HM 8, QPM maize- QPM 9 and sweet corn- HSC I). The incubation period of all the R. solani isolates ranged from 3 to 7 days with average disease score ranged from 2.1 to 7.1depending upon the host and isolate interaction after artificial inoculation. Disease intensity of all the RSS isolates on maize hybrids varied from 8.1 to 52.1% while isolates RSS 29 (New Delhi) and RSS 1 (Karnal) were found highly virulent, in comparison to other forty nine RSS isolates. As these isolates were highly virulent on maize hybrids with average disease score ranged from 6.2 to 7.1, whereas the isolates RS 30 (Dholi) and RS 31 (Dhaula Kuan) were recorded as least virulent, showed resistant disease reaction with average disease score ranged from 1.5 to 2.4.

The methods of recognition and diagnosis of main maize leaf diseases using machine vision were studied in the paper. Threshold method was adopted to do image segmentation, and area-marking method was used calculating the num of disease as well as wiping off redundancy dots. And then Freeman link code was used to calculate form feature. Finally diseases were deduced according to bin-tree search method. In the research, the exclusive feature of main maize leaf diseases was presented, and the flow of disease diagnosis was confirmed and the recognition models were developed. The results indicate that the precision of five kinds of maize disease recognition is higher than 80%. It shows that this method is available for recognizing maize disease, and provides technique support for the automatic recognition of disease by compiling the system with reasonable process flow.

There are thousands of grains and fruits grown on this planet every day. In order to cope with the needs of the people, new farming practices are constantly being invented. In addition, the discovery and use of new technologies, which can easily and quickly detect plant diseases. In this research, we tried to detect the diseases and provided their remedies through the images of the infected leaf of a grain "Corn" along with a fruit "Peach" with the help of computer science by using Artificial Intelligence. Since corn is a very popular crop and food, it is cultivated throughout the world. In particular, the demand and popularity of corn in Bangladesh is a skyscraper. Even though peach is not as much popular as corn in Bangladesh but in recent time it is gaining attraction of people in the fruit market. But due to timely consultation and availability of available technology, maize farmers and the farmers who are interested in cultivating peach professionally, often face the attack of insects and diseases. As soon as the crop gets destroyed, the farmers' dividends also get spoiled which is a huge loss for all. So, this research will predict the diseases of corn leaf as well as peach plant leaf and pass down their remedy soon after the affected leaf's picture is given as an input. In this research we have used image processing, convolutional neural network (CNN) algorithm to train our dataset. In the end, our system successfully achieved validation accuracy of over 99.28%. This research is going to help numerous farmers all over the world especially the farmers of our country Bangladesh to increase the production rate of corn and the popularity rate of peach by reducing the attack of insects and diseases in time.

Plant diseases are the main factor in plant mortality and destruction, especially in trees. Early discovery, however, can assist to manage and treat this issue efficiently. To increase output, crop and plant lesions are detected and stopped as soon as feasible. Because it relies solely on visual observation, manual inspection of plant leaf diseases is time-consuming and expensive. The authors offer methods for identifying and categorizing plant leaf diseases using computer vision. Pre-processing original images to visualize contaminated areas, feature extraction from unprocessed or segmented images, feature fusion, feature selection, and classification are a few examples of computer vision approaches. The fusion technique is used to combine the target's numerical data features, which go beyond the picture, with the extracted image features to increase the target's feature representation. The following are the principal issues that researchers found in the literature: Low-contrast infected regions. Extract redundant and irrelevant information, which degrades classification accuracy; Redundant and irrelevant information may lengthen computation times and the targeted models performance will suffer as a result. This study proposed a framework for classifying plant leaf diseases based on the best feature selection and a deep learning fusion model. In the suggested approach, contrast is first enhanced using a pre-processing model, and then the issue of an unbalanced dataset is resolved via data augmentation. The proposed Deep Fusion Learning Model (DFLM) shows an accuracy of 98.8% in comparison with other models.

Highlights Improving the robustness of maize leaf disease classification models in complex environments using multiple data enhancement methods. Fusing ResNet50 with the triplet attention mechanism to improve feature extraction. Reducing the use of labeled data to some extent by using SimCLR. The classification accuracy of four different leaf types was 91.87%, which was higher than other compared models. Abstract. Deep learning methods for classifying maize leaf diseases often need a lot of labeled data for training during the model training phase, but labeling data is sometimes difficult and expensive. In addition, complicated environmental circumstances can readily impede the recognition effect. In order to solve this issue, SimCLR and the triplet attention mechanism were coupled to create a system for classifying maize leaf diseases. Firstly, to increase the robustness of the model, several data enhancement techniques were used to imitate the interference of complex components in the natural environment. Secondly, the use of labeled data was minimized by learning the similarity between related categories using a self-supervised SimCLR framework. The feature extraction network simultaneously employed a triplet attention mechanism to enhance the model’s ability to extract information about the interactions between space and channel, improve attention to discriminative features, and lessen interference from complex factors. In order to verify the effects of incorporating SimCLR and triplet attention mechanisms, this work used three different types of maize leaf diseases and healthy leaves as experimental objects. The final enhanced model was compared and analyzed with seven widely used image classification models. The experimental results demonstrated that the maize leaf disease classification model suggested in this article effectively enhanced the classification performance of maize leaf diseases containing complex factors of interference with the test set, with the network model proposed in this article having an average classification accuracy of 91.87%, better than the seven comparative models and requiring no additional labeled data during the training process. Keywords: Image classification, Maize leaf disease, Self-supervised learning, SimCLR, Triplet attention module.

The classification of plant leaf diseases is a critical step in safeguarding global food security and ensuring sustainable agricultural productivity.Recent advances in artificial intelligence (AI) and deep learning have accelerated the development of automated disease detection systems, improving both efficiency and diagnostic accuracy.This study introduces a novel hybrid strategy that integrates Convolutional Neural Networks (CNN) with traditional machine learning classifiers, specifically Support Vector Machines (SVM) and XGBoost, to leverage the complementary strengths of both paradigms.Two hybrid variants, CNN+SVM and CNN+XGBoost, are proposed, alongside the use of Particle Swarm Optimization (PSO) for feature selection, enabling the extraction of the most informative features for robust classification.The dataset used for experimentation comprises 15,000 maize leaf images, covering four major diseases: Northern Corn Leaf Blight, Common Rust, Brown Spot, and Gray Leaf Spot, along with healthy samples.Experimental evaluations confirm that the hybrid models outperform standalone CNN classifiers, achieving superior accuracy and generalization.Specifically, CNN+XGBoost achieved 95.2% accuracy, 93% precision, 96.3% recall, and 94.3% F1-score, while CNN+SVM achieved 92.7% accuracy, 91.1% precision, 94% recall, and 92.2% F1-score.These results demonstrate the synergistic benefits of combining CNNs with classical classifiers, while PSO significantly enhanced classification outcomes by identifying highly discriminative features.This research contributes to the advancement of precision agriculture by presenting a scalable hybrid framework for maize disease detection, offering timely diagnostic insights for farmers and promoting sustainable crop management practices.

Corn is an important commodity in Indonesia and one of the world’s staple foods. According to FAO in 2017, disease problems often hamper corn production. The main problems that affect crop yields are diseases that damage corn leaves, including rust, spot, blight and downy mildew. Manual disease identification on corn leaves has limitations in consistency and scalability. A digital image processing system is needed to increase the speed and accuracy of recognition. Classification of types of corn leaf disease is needed so that farmers can distinguish types of corn leaf disease. Therefore, to identify four forms of maize leaf disease, this study tested a CNN model using the EfficientNetB3 and MobileNetV2 architectures. The data used to evaluate these two models is divided into training and testing subsets. Based on test results with 50 epochs, EfficientNetB3 obtained 93.20% accuracy with a loss of 0.0850, while MobileNetV2 obtained 92.48% accuracy with a loss of 0.19020. When the test data is evaluated, EfficientNetB3 performs better than MobileNetV2. MobileNetV2 has limitations in handling complex feature representation on very complex data. On the other hand, although it provides better results, EfficientNetB3 has the disadvantage of high computing and memory resource requirements, which can hamper efficiency in practical implementation. Based on these findings, EfficientNetB3 is recommended because it performs better in maize leaf disease classification, shows smaller losses and higher accuracy than MobileNetV2. By using this model, corn plant diseases can be identified efficiently and precisely, thereby improving disease control and producing more productive corn plants.

The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it's similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.

This experiment was conducted to determine the effect of defoliation time on the productivity of maize, forage and in vitro digestibility of maize leaves (ML). The response of grain yield, plant height, days to 50% silking, ear height and leaf yield as well as the chemical composition of ML variety "Obasuper" to defoliation time were evaluated in a randomized complete block design with four defoliation treatments 3, 6, 9 weeks after planting (WAP) and undefoliated treatment replicated four times. Results showed that defoliating maize at 9 WAP and beyond did not have any significant (P>0.05) effect on the grain and leaf yield as well as other yield components of maize. Grain and leaf yield at 9 WAP averaged 2778.35 and 975.74 DM kg/ha respectively. The DM, crude protein (CP) and fibre fractions contents of ML varied significantly (P<0.05) among the defoliation treatments with maize defoliated at 9 WAP having a CP content of 12.6%. Ether extract and ash contents were not affected (P>0.05) by defoliation time. The in vitro digestibility of ML decreased significantly (P<0.05) with increase in the time of defoliation ranging from 59.2 and 66.4%. IT was concluded that maize variety "Obasuper" can produce forage of high quality and quantity when defoliated 9 weeks after planting wihout any anyadverse effect on the grain yield of maize.

Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.

Maize leaf disease image enhancement algorithm using TFEGAN