YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11

Maize leaf disease recognition based on improved MSRCR and OSCRNet

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.

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.

Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.

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.

Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster.

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.

Maize leaf disease image enhancement algorithm using TFEGAN

In recent years, a lot of research has been conducted in the field of object detection for aerial image, and many effective methods have emerged. This paper enhances the existing Faster RCNN model to achieve better detection accuracy. We improve the feature extraction of Faster RCNN model using multi-scale feature fusion. Compared with the traditional Faster RCNN model, the accuracy on mAP is improved by 1.06% by applying the proposed method. The visual effects and numerical results of object detection verify the improvement of the proposed method.

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.

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.

This study aims to improve the accuracy of landslide detection in satellite images by combining two object detection models based on a faster region-based convolutional neural network (Faster R-CNN) with a classification decision tree. The proposed method combines the predicted results from the two Faster R-CNN models and classifies their features with a classification decision tree to generate a bounding-box that surrounds the landslide area in the input image. The first Faster R-CNN model is trained by using a training set of color images (RGB images). The second model is trained by using grayscale images that represent digital elevation models (DEMs). The results from both models are used to construct features for training a classification decision tree. The resulting bounding-box is selected from the following four classes: the box obtained from the RGB model, the box obtained from the DEM model, the intersection of those two boxes, and the smallest box that contains the union of them. The evaluation results show that the proposed method is better than the RGB model in terms of accuracy, precision, recall, F-measure, and Intersection-over-Union (IoU) score. It is slightly better than the DEM model in almost all evaluation metrics, except the precision.

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.

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.

One of the most essential and current research areas is waste hazard monitoring in floating water and water reservoirs. The increasing population and urbanisation contribute to waste hazards, negatively impacting water quality, human health, and environmental resources. Many research methods have focused on applying metaheuristic and image-processing techniques to analyse and detect waste hazards in floating water. The detection efficiency was good; however, their computational complexity was high and not cost-effective. Additionally, it takes longer to analyse the data. Coastal, riverine, and seaside areas require effective detection and monitoring systems to generate alerts for waste-hazard removal. Otherwise, these hazards pose numerous health risks to the public and degrade water quality. However, it remains a complex technological challenge due to real-time constraints, environmental changes, and the lack of automation in traditional systems. This paper addresses this major challenge and aims to design and implement an IoT-integrated deep learning model incorporating principal component analysis (PCA), grey-level co-occurrence matrix (GLCM), and Fast R-CNN to enable automatic, optimal waste-hazard detection in dynamic floating and static water bodies. Various IoT sensors and edge devices are installed in water bodies to collect data. Initially, the PCA method analyses the data and improves the entire Fast R-CNN model by efficiently extracting, compressing, and denoising features, while GLCM captures discriminative textural information. Moreover, the Fast R-CNN model reduces computational complexity while improving detection and classification accuracy. Both input and predicted data are securely transmitted through fog computing and interconnected throughout the entire architecture. The deep learning model is implemented with IoT data, and the results are validated. The output demonstrates that PCA-GLCM-integrated Fast R-CNN provides high accuracy in detecting different types of waste hazards with a lower false-positive rate and reduced latency.