A System for Identifying Deficiency Elements in Apple Leaf Disease Based on Deep Learning

Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms have been proposed to diagnose apple leaf diseases, but most of them are designed to run on high-performance GPUs. This potentially limits their application in the field, in which mobile devices are expected to be used to perform computer vision-based disease diagnosis on the spot. In this paper, we propose a lightweight one-stage network, called the Mobile Ghost Attention YOLO network (MGA-YOLO), which enables real-time diagnosis of apple leaf diseases on mobile devices. We also built a dataset, called the Apple Leaf Disease Object Detection dataset (ALDOD), that contains 8,838 images of healthy and infected apple leaves with complex backgrounds, collected from existing public datasets. In our proposed model, we replaced the ordinary convolution with the Ghost module to significantly reduce the number of parameters and floating point operations (FLOPs) due to cheap operations of the Ghost module. We then constructed the Mobile Inverted Residual Bottleneck Convolution and integrated the Convolutional Block Attention Module (CBAM) into the YOLO network to improve its performance on feature extraction. Finally, an extra prediction head was added to detect extra large objects. We tested our method on the ALDOD testing set. Results showed that our method outperformed other state-of-the-art methods with the highest mAP of 89.3%, the smallest model size of only 10.34 MB and the highest frames per second (FPS) of 84.1 on the GPU server. The proposed model was also tested on a mobile phone, which achieved 12.5 FPS. In addition, by applying image augmentation techniques on the dataset, mAP of our method was further improved to 94.0%. These results suggest that our model can accurately and efficiently detect apple leaf diseases and can be used for real-time detection of apple leaf diseases on mobile devices.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

Control of foliar diseases of tea with Xanthium strumarium leaf extract

Apple leaf diseases have significant impacts on apple quality and productivity. So, the implementation of accurate disease detection in the early stages is a powerful guarantee for the rapid and high-quality development of the apple industry. However, early apple leaf disease often represents very small size disease spots, which makes the detection of early apple leaf disease a challenge for existing deep learning-based detection models. In this paper, a novel detection model called Representation-Enhanced RCNN(RE-RCNN) is proposed to perform accurate detection of early apple leaf disease spots. Firstly, an object-enhanced branch is proposed to achieve feature enhancement of small disease spots by introducing small disease spots feature enrichment extractor (SDSFEE). Secondly, a SCMLoss is proposed to balance the inter-class differences of various size disease spots under the same category. Thirdly, an one2one computation strategy is leveraged to sample data reasonably during the training process. From the final experimental results, it can be seen that the proposed model could achieve outstanding performance on the early apple leaf disease detection task.

The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

Morpho-cultural and molecular phylogenetic characterisation of Curvularia verruculosa causing leaf spot and blight disease on Strychnos potatorum – A new record from India

Apple leaf diseases seriously affect the quality of apples and may lead to yield losses, detecting apple leaf diseases accurately can prevent diseases from spreading and promote the healthy growth of the industry. However, recent studies cannot achieve accurate detection of leaf diseases with high accuracy because the lesions are of different sizes. So, this paper proposed a novel apple leaf disease detection method called VMF-SSD (V-space-based Multi-scale Feature-fusion SSD), which is designed to extract more reliable multi-scale feature representations for varied sizes of diseased spots and improve the final detection performance. The multi-scale feature extraction is established with multi-scale feature representation to further improve the disease detection performance, especially for small spots. After that, a V-space-based location branch is presented to enhance the texture feature information and help further identify disease spot location. Finally, attention mechanisms are utilized to automatically learn the importance of feature channels at different scales for distinguishing diseased spots of different sizes. Experimental results showed that the VMF-SSD method achieves 83.19% mAP and obtains the detection speed of 27.53 FPS on the test set, which indicates that the proposed VMF-SSD method can achieve competitive performance on apple leaf diseases detection task and satisfy the requirements of agricultural production applications.

One of the most influential factors in the reduction of agricultural products is diseases in plants. In the meantime, every year, apple trees suffer huge losses in terms of plant diseases, and the yield of their product decreases. Therefore, the study of the identification of apple leaf diseases is of great importance. The use of machine vision and machine learning algorithms have greatly helped pathologists in this field. Accurate identification of effective features such as color, texture, and shape in the diagnosis of plant diseases in the processing of images taken from the diseased plant plays an important role in the classification of disease groups. In this study, a new optimized method named slime mould optimization algorithm and SVM classifier were combined to diagnose three apple tree diseases, i.e., black spot, black rot or frog eye leaf spot, and cedar rust. The results obtained in this method provided 12 effective features out of 159 features extracted from disease images, and the accuracy of disease classification was 96.21%.

Intermediate wheatgrass [Elytrigia intermedia (Host) Nevski] including the pubescent subspecies [E. intermedia subsp, barbulatum (Schur) A. Löve], is an important introduced cool‐season grass the plains of northern USA and southern Canada. It is susceptible, however, to foliar diseases, primarily those caused by Cochliobolus sativus, Pyrenophora tritici‐repentis, and Leptosphaeria nodorum. The primary objective of this study was to evaluate the effect of naturally occurring foliar diseases on the nutritive quality of a diverse group of intermediate wheatgrass germplasm selected for resistance or susceptibility to these diseases. A secondary objective was to examine the effect of plant height on nutritive quality and the development of foliar diseases. Herbage samples were collected from resistant and susceptible plants selected from 12 cultivars or strains on 29 July 1983 and 2 July 1985. Susceptible plants infected with foliar diseases (2‐yr means) were lower in N, and higher in neutral‐detergent fiber (NDF) (P < 0.05) than resistant plants. In 1985, susceptible plants had higher concentrations of acid‐detergent fiber (ADF), but there was no significant (P > 0.05) difference between resistant and susceptible plants with respect to cellulose and hemicellulose expressed on an NDF basis, indicating the affect of foliar diseases was on the cell contents, not on the cell walls. Correlation coefficients between disease ratings and in vitro digestible organic matter, N, and NDF were r = −0.27, −0.71, and 0.76, respectively, in 1983. Correlation coefficients involving these entities were lower in 1985. Plant height did not not affect the plants' resistance or susceptibility to foliar diseases. Results from this study demonstrate over a representative germplasm base that nutritive quality of intermediate wheatgrass is diminished by leaf spot diseases, and that leaf spot resistant plants maintain a higher nutritive quality in the presence of disease.

Association of cropping practices, cropping areas, and foliar diseases of common bean (Phaseolus vulgaris L.) in Ethiopia

Dehydrogenase activity in the rice plant tissues inoculated with Cochliobolus miyabeanus (Helminthosporium leaf-spot fungus) and in the causal fungus was investigated using blue tetrazolium (BT). This reaction was positive in the sections of healthy leaf tissues after 2 hours' incubation at 30∼32°C and the rate of the staining became constant after 8 hours' incubation. The reaction was negative when the sections were pretreated with formalin (10%, 1hr.) or treated at 50∼60°C, 10min. In leaf tissues, the parenchymatous cells were most strongly stained and the sites of the enzyme activity seemed to lie on grana-like region in chloroplasts.The leaf spot caused by the present fungus became visible 20 hours after inoculation. The dehydrogenase activity in this early stage of infection was demonstrated in cells near browned epidermis. With the enlargement of spotted area, the tissue around the lesion stained with BT; but the reaction became constant 48 hours after inoculation, although the enlargement of the lesion was still proceeding. In the typical leaf spot produced 5∼7 days after inoculation, localization and intensity of formazan deposition could not be distinguished from those in healthy leaf tissue.The reaction was weak in leaf-sheath cells even when the pathogen penetrated them. On the other hand, the penetrating hyphae, especially the appressoria and the germ tubes, showed strong, reaction. When the spores germinated on slide-glass, the reaction was detected on the terminal cells of the spores and on the germ tubes as well as on the appressoria. This positive reaction on spores of the present fungus seemed to localize on mitochondria of the fungus.

The traditional teaching mode, mainly characterized by "cramming" instruction, often overlooks the absorption of knowledge by students during the teaching process. Even in recent years, the emerging "flipped classroom" model heavily relies on teachers' experience and expertise, making it challenging to quantify students' classroom learning states. Among young teachers lacking teaching experience, the replication of excellent teaching models faces difficulties. With the rapid development of artificial intelligence technology, deep learning and other AI techniques are increasingly applied to people's lives. Additionally, emotion computing technology is also becoming more sophisticated. In the field of education, the use of AI technology holds the potential to help teachers systematically and comprehensively assess teaching quality, promoting the sharing of high-quality educational resources nationally and globally. Existing emotion detection technologies often focus on single techniques such as image recognition or natural language processing, resulting in low accuracy in discerning human emotions and making it difficult to accurately identify emotional changes in complex scenarios such as classrooms. Furthermore, there is a lack of comprehensive emotional statistical analysis methods for audio and visual data. In light of these, we have designed and implemented a multi-modal emotion recognition and analysis system. Through testing in classroom settings, this system has the potential to assist classroom teaching and enhance teaching quality.

In agriculture, plant pests and diseases are one of the major factors that reduce fruit and vegetable yields. Early detection of foliar diseases, which cause significant yield losses in apple production, is of great importance for the sustainability of agricultural production. The subjective and inefficient nature of traditional manual and tool-based diagnostic methods is insufficient to provide solutions that meet scientific and production standards. In this context, pattern recognition and deep learning techniques offer more effective alternatives for automatic image analysis and classification. In this study, Vision Transformers are used for disease classification from apple leaf images. The dataset consists of images of healthy apple leaves and apple leaves with different diseases. The results show that Vision Transformers is effective in classifying apple leaf diseases with high accuracy. This study makes an important contribution to increasing the use of digital technologies in agriculture and plant health monitoring.