DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection

Efficient identification of apple leaf diseases (ALDs) can reduce the use of pesticides and increase the quality of apple fruit, which is of significance to smart agriculture. However, existing research into identifying ALDs lacks models/methods that satisfy efficient identification in the wild environment, hindering the application of smart agriculture in the apple industry. Therefore, this paper explores an ACCURATE, LIGHTWEIGHT, and ROBUST convolutional neural network (CNN) called EfficientNet-MG, improving the conventional EfficientNet network by the multistage feature fusion (MSFF) method and gaussian error linear unit (GELU) activation function. The shallow and deep convolutional layers usually contain detailed and semantic information, respectively, but conventional EfficientNets do not fully utilize the different stage convolutional layers. Thus, MSFF was adopted to improve the semantic representation capacity of the last layer of features, and GELU was used to adapt to complicated tasks. Further, a comprehensive ALD dataset called AppleLeaf9 was constructed for the wild environment. The experimental results show that EfficientNet-MG achieves a higher accuracy (99.11%) and fewer parameters (8.42 M) than the five classical CNN models, thus proving that EfficientNet-MG achieves more competitive results on ALD identification.

/pdf/efficient-identification-of-apple-leaf-diseases-in-the-wild-20nviuno.pdf

Efficient Identification of Apple Leaf Diseases in the Wild Using Convolutional Neural Networks

Apple leaf diseases threaten apple orchard sustain ability and production worldwide. Accurate and early identification is essential for the successful care and control of many disorders. This article suggests a convolutional neural network (CNN) model for classifying apple leaf diseases based on federated learning. Using a dataset of 7,832 photos with a resolution of 224×224 pixels and five distinct classes of apple leaf disease, the model is intended to identify these diseases. Assuring data confidentiality and privacy, the federated learning architecture enables decentralized model training across several clients. The suggested model was tested on four customers, and the results showed that the model's precision, recall, and accuracy ranged from 93.51% to 94.92%, 93.22% to 94.72%, 93.33% to 94.81%, and 0.97 to 0.98, respectively. These findings show how the federated learning CNN model outperforms alternative deep learning architectures and conventional machine learning techniques in identifying apple leaf diseases. The model generalizes well to different data distributions based on consistent client performance, which qualifies it for use in practical applications. Future research should address federated learning's drawbacks and restrictions, such as heterogeneous data distribution, model convergence, and resource limits. Additionally, investigating future uses in agriculture and plant disease control may result in a better judgment and more environmentally friendly agricultural methods.

Apple Leaf Disease Recognition: A Robust Federated Learning CNN Methodology

Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.

/pdf/apple-net-a-model-based-on-improved-yolov5-to-detect-the-7dpaxtux.pdf

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Abstract Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners. 

Image‐based crop disease detection using machine learning

Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Although, there exist several CNN models for crop disease detection with comparable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Abstract. Fusion of complementary information from multisensor data is of great importance for identifying the land covers. However, integration of multisource information is a challenging task. A framework is developed to integrate hyperspectral and LiDAR data for land cover classification. In the proposed method, sparse stacked autoencoders are used to represent the spectral and spatial information in a compact form. The spatial information is extracted both from hyperspectral and LiDAR data using morphological operators. The encoded spectral and spatial features are combined with elevation information to form a joint feature vector. The joint features are fed to a convolutional neural network (CNN) classifier to classify the land covers. The CNN classifier is a hybrid three- and two-dimensional (3D)–(2D) model having three 3D convolutional layers and one 2D convolutional layer. The experiments are carried out on two datasets Houston and Samford to evaluate the performance of the proposed method. The results have demonstrated the effectiveness of the method with global κ  =  0.9285 and global naive accuracy (OA) of 93.44% for Houston data. For Samford data, it achieves κ  =  0.9811 and OA  =  98.93  %  .

Fusion of hyperspectral and LiDAR data using sparse stacked autoencoder for land cover classification with 3D-2D convolutional neural network

Paddy yield prediction based on 2D images of rice panicles using regression techniques

Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning

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Papers

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Fusion of hyperspectral and LiDAR data using sparse stacked autoencoder for land cover classification with 3D-2D convolutional neural network

Paddy yield prediction based on 2D images of rice panicles using regression techniques

Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning