Analysis of banana plant health using machine learning techniques.

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Smartphone image dataset to distinguish healthy and unhealthy leaves in papaya orchards in Bangladesh.

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.

Dataset of chilli and onion plant leaf images for classification and detection.

This article presents the chili and onion leaf (COLD) dataset, which focuses on the leaves of chili and onion plants, scientifically known as Allium cepa and capsicum. The presence of various diseases such as Purple blotch, Stemphylium leaf blight, Colletotrichum leaf blight, and Iris yellow spot virus in onions, as well as Cercospora leaf spot, powdery mildew, Murda complex syndrome, and nutrition deficiency in chili, have had a significant negative effect on onion and chili production. As a consequence, farmers have incurred financial losses. Computer vision and image-processing algorithms have been widely used in recent years for a range of applications, such as diagnosing and categorizing plant leaf diseases. In this paper we introduced a detailed chilli and onion leaf dataset gathered from Chilwadigi village with varying climatic conditions in Karnataka. The dataset contains a variety of chili and onion leaf categories carefully selected to tackle the complex challenges of categorizing leaf images taken in natural environments. Dealing with challenges such as subtle inter-class similarities, changes in lighting, and differences in background conditions like different foliage arrangements and varying light levels. We carefully documented chilli and onion leaves from various angles using high resolution camera to create a diverse and reliable dataset. The dataset on chilli leaves is set to be a valuable resource for enhancing computer vision algorithms, from traditional deep learning models to cutting-edge vision transformer architectures. This will help in creating advanced image recognition systems specifically designed for identifying chilli plants. By making this dataset publicly accessible, our goal is to empower researchers to develop new computer vision techniques to tackle the unique challenges of chilli and onion leaf recognition. You can access the dataset for free at the following DOI number: http://doi.org/10.17632/7nxxn4gj5s.3 and http://doi.org/10.17632/tf9dtfz9m6.3.

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm's evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity.

Maize leaf diseases significantly impact yield and quality. However, recognizing these diseases from images taken in natural environments is challenging due to complex backgrounds and high similarity of disease spots between classes.This study proposes a lightweight multi-level attention fusion network (LFMNet) which can identify maize leaf diseases with high similarity in natural environment. The main components of LFMNet are PMFFM and MAttion blocks, with three key improvements relative to existing essential blocks. First, it improves the adaptability to the change of maize leaf disease scale through the dense connection of partial convolution with different expansion rates and reduces the parameters at the same time. The second improvement is that it replaces a adaptable pooling kernel according to the size of the input feature map on the original PPA, and the convolution layer to reshape to enhance the feature extraction of maize leaves under complex background. The third improvement is that it replaces different pooling kernels to obtain features of different scales based on GMDC and generate feature weighting matrix to enhance important regional features. Experimental results show that the accuracy of the LFMNet model on the test dataset reaches 94.12%, which is better than the existing heavyweight networks, such as ResNet50 and Inception v3, and lightweight networks such as DenseNet 121,MobileNet(V3-large) and ShuffleNet V2. The number of parameters is only 0.88m, which is better than the current mainstream lightweight network. It is also effective to identify the disease types with similar disease spots in leaves.

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

Empowering agricultural research: A comprehensive custard apple (Annona squamosa) disease dataset for precise detection.

The Custard Apple, known as sugar apple or sweetsop, spans diverse regions like India, Portugal, Thailand, Cuba, and the West Indies. This dataset holds 8226 images of Custard Apple (Annona squamosa) fruit and leaf diseases, categorized into six types: Athracnose, Blank Canker, Diplodia Rot, Leaf Spot on fruit, Leaf Spot on leaf, and Mealy Bug. It's a key resource for refining machine learning algorithms focused on detecting and classifying diseases in Custard Apple plants. Utilizing methods like deep learning, feature extraction, and pattern recognition, this dataset sharpens automated disease identification precision. Its extensive range suits testing and training disease identification techniques. Public access fosters collaboration, fast-tracking plant pathology advancements and supporting Custard Apple plant sustainability. This dataset fosters collaborative efforts, aiding disease prevention techniques to boost Custard Apple yield and refine farming. It enhances disease identification, monitoring, and management in Custard Apple production, aiming to elevate agricultural practices and crop yields.

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging.

BACKGROUND: Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. RESULTS: Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. CONCLUSIONS: This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.

Recognition of Wheat Leaf Diseases Using Lightweight Convolutional Neural Networks against Complex Backgrounds.

Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the performance and training speed of the model in CNNs. This study employed six training strategies, including Adam, SGD, Adam + StepLR, SGD + StepLR, Warm-up + Cosine annealing + SGD, Warm-up + Cosine, and annealing + Adam, with three initial learning rates (0.05, 0.01, and 0.001). Using the wheat stripe rust, wheat powdery mildew, and healthy wheat datasets, five lightweight CNN models, namely MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2, were evaluated. The results showed that upon combining the SGD + StepLR with the initial learning rate of 0.001, the MnasNet obtained the highest recognition accuracy of 98.65%. The accuracy increased by 1.1% as compared to that obtained with the training strategy with a fixed learning rate, and the size of the parameters was only 19.09 M. The above results indicated that the MnasNet was appropriate for porting to the mobile terminal and efficient for automatically identifying wheat leaf diseases.

Diaporthe Species on Palms: Molecular Re-Assessment and Species Boundaries Delimitation in the D. arecae Species Complex.

Due to cryptic diversification, phenotypic plasticity and host associations, multilocus phylogenetic analyses have become the most important tool in accurately identifying and circumscribing species in the Diaporthe genus. However, the application of the genealogical concordance criterion has often been overlooked, ultimately leading to an exponential increase in novel Diaporthe spp. Due to the large number of species, many lineages remain poorly understood under the so-called species complexes. For this reason, a robust delimitation of the species boundaries in Diaporthe is still an ongoing challenge. Therefore, the present study aimed to resolve the species boundaries of the Diaporthe arecae species complex (DASC) by implementing an integrative taxonomic approach. The Genealogical Phylogenetic Species Recognition (GCPSR) principle revealed incongruences between the individual gene genealogies. Moreover, the Poisson Tree Processes' (PTPs) coalescent-based species delimitation models identified three well-delimited subclades represented by the species D. arecae, D. chiangmaiensis and D. smilacicola. These results evidence that all species previously described in the D. arecae subclade are conspecific, which is coherent with the morphological indistinctiveness observed and the absence of reproductive isolation and barriers to gene flow. Thus, 52 Diaporthe spp. are reduced to synonymy under D. arecae. Recent population expansion and the possibility of incomplete lineage sorting suggested that the D. arecae subclade may be considered as ongoing evolving lineages under active divergence and speciation. Hence, the genetic diversity and intraspecific variability of D. arecae in the context of current global climate change and the role of D. arecae as a pathogen on palm trees and other hosts are also discussed. This study illustrates that species in Diaporthe are highly overestimated, and highlights the relevance of applying an integrative taxonomic approach to accurately circumscribe the species boundaries in the genus Diaporthe.

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model.

Introduction: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method: This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results: The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds.

Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel2) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases.

Dataset of groundnut plant leaf images for classification and detection.

The use of machine learning is rapidly expanding across many industries, including agriculture and the IT sector. However, data is essential for machine learning models, and a substantial amount of data is required prior to training a model. We have collected data of groundnut plant leaves in the form of digital photographs taken in the Koppal (Karnataka, India) area with the assistance of a pathologist in natural settings. Images of leaves are categorized into six distinct groups according to their condition. Collected images are pre-processed and the processed images of groundnut leaves are kept in 6 folders as: the "healthy leaves" folder with 1871 images, the "early leaf spot" folder with 1731 images, the "late leaf spot" folder with 1896 images, the "Nutrition deficiency" folder with 1665 images, the "rust" folder with 1724 images, and the "early rust" folder with 1474 images. The total number of images in the dataset is 10361. This dataset will be useful to train and validate deep learning and machine learning algorithms for groundnut leaf disease classification and recognition. Disease detection in plants is crucial for limiting crop losses and our dataset will help disease detection in groundnut plants. This dataset is freely accessible to public at https://data.mendeley.com/datasets/22p2vcbxfk/3 and at https://doi.org/10.17632/22p2vcbxfk.3.

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model.

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.

Tomato leaf disease recognition based on multi-task distillation learning.

Introduction: Tomato leaf diseases can cause major yield and quality losses. Computer vision techniques for automated disease recognition show promise but face challenges like symptom variations, limited labeled data, and model complexity. Methods: Prior works explored hand-crafted and deep learning features for tomato disease classification and multi-task severity prediction, but did not sufficiently exploit the shared and unique knowledge between these tasks. We present a novel multi-task distillation learning (MTDL) framework for comprehensive diagnosis of tomato leaf diseases. It employs knowledge disentanglement, mutual learning, and knowledge integration through a multi-stage strategy to leverage the complementary nature of classification and severity prediction. Results: Experiments show our framework improves performance while reducing model complexity. The MTDL-optimized EfficientNet outperforms single-task ResNet101 in classification accuracy by 0.68% and severity estimation by 1.52%, using only 9.46% of its parameters. Discussion: The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today's era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40-60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

MDAM-DRNet: Dual Channel Residual Network With Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection.

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases' spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model's ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases.

CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases.

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases.

Fine-Grained Grape Leaf Diseases Recognition Method Based on Improved Lightweight Attention Network.

Real-time dynamic monitoring of orchard grape leaf diseases can greatly improve the efficiency of disease control and is of great significance to the healthy and stable development of the grape industry. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Therefore, an efficient method is urgently needed for real-time dynamic monitoring of orchard grape diseases. The classical deep learning network can achieve high accuracy in recognizing grape leaf diseases; however, the large amount of model parameters requires huge computing resources, and it is difficult to deploy to actual application scenarios. To solve the above problems, a cross-channel interactive attention mechanism-based lightweight model (ECA-SNet) is proposed. First, based on 6,867 collected images of five common leaf diseases of measles, black rot, downy mildew, leaf blight, powdery mildew, and healthy leaves, image augmentation techniques are used to construct the training, validation, and test set. Then, with ShuffleNet-v2 as the backbone, an efficient channel attention strategy is introduced to strengthen the ability of the model for extracting fine-grained lesion features. Ultimately, the efficient lightweight model ECA-SNet is obtained by further simplifying the network layer structure. The model parameters amount of ECA-SNet 0.5× is only 24.6% of ShuffleNet-v2 1.0×, but the recognition accuracy is increased by 3.66 percentage points to 98.86%, and FLOPs are only 37.4 M, which means the performance is significantly better than other commonly used lightweight methods. Although the similarity of fine-grained features of different diseases image is relatively high, the average F1-score of the proposed lightweight model can still reach 0.988, which means the model has strong stability and anti-interference ability. The results show that the lightweight attention mechanism model proposed in this paper can efficiently use image fine-grained information to diagnose orchard grape leaf diseases at a low computing cost.

Rice leaf diseases prediction using deep neural networks with transfer learning.

Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, virus, etc. For disease management, farmers spending lot of time and resources and they detect the diseases through their penniless naked eye approach which leads to unhealthy farming. The advancement of technical support in agriculture greatly assists for automatic identification of infectious organisms in the rice plants leaves. The convolutional neural network algorithm (CNN) is one of the algorithms in deep learning has been triumphantly invoked for solving computer vision problems like image classification, object segmentation, image analysis, etc. In our work, InceptionResNetV2 is a type of CNN model utilized with transfer learning approach for recognizing diseases in rice leaf images. The parameters of the proposed model is optimized for the classification task and obtained a good accuracy of 95.67%.