A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools

PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network

Today, agriculture plays an important role due to population growth and increasing demand for food. Diseases caused by bacteria, fungi, and viruses are an effective factor in product quality. Accurate diagnosis and identification of plant diseases are necessary to developing intelligent and modern agricultural production. Plant diseases can affect leaves during cultivation, causing serious damage to crop quality, yield, and economic value. Therefore, in the farming industry, the identification of leaf diseases plays a vital role, and since there are many types of leaf diseases and pests, their pathology is very complex. Manual diagnosis of these diseases is time-consuming and requires expertise in this field. Deep learning techniques have introduced several smart solutions to detect and control plant pests and diseases effectively. Deep learning is a branch of artificial intelligence that has received a lot of attention in recent years, with the benefits of automatic feature learning and extraction. Convolutional neural network (CNN) is one of the most important networks in the field of deep learning. CNN-based deep learning methods have made significant progress in image classification. Several factors related to plant disease detection using deep learning techniques need to be considered to develop a robust system for accurate disease management. This article discussed and studied a deep learning-based method using CNN networks and a series of preprocessing with entropy filters to detect tomato leaf diseases. The proposed model achieved 98.42% accuracy for the classification of 11 classes with a loss function of 0.08. We recommend using the proposed network because it requires less time and memory than previous works to achieve the desired accuracy.

Plant diseases have the potential to damage the livelihoods of farmers and impede their capacity to generate an adequate quantity of food. Diagnosis and early detection of plant diseases are critical for their effective management and control. Leaf analysis shows great potential as a method for forecasting disease stages due to its ability to detect subtle alterations in leaf physiology and appearance that may occur prior to the onset of conspicuous symptoms. By utilizing the algorithms of machine learning and deep learning, it is possible to classify characteristics extracted from photographs of leaves into distinct disease stages. Recent studies have demonstrated the potential of these algorithms, as they have achieved remarkable accuracy in disease stage prediction despite having limited training data. The integration of machine learning and deep learning techniques with foliage analysis holds promise for revolutionizing plant disease management through the provision of timely identification, accurate diagnosis, and customized treatment. In order to formulate efficacious disease management strategies, precise determination of the developmental stage of plant diseases is imperative. Scholars are presently devising novel approaches to identify the stages of plant diseases by employing diverse methodologies, including spectroscopy, machine learning, and image processing. This can benefit producers in substantial economic and environmental ways, as well as contribute to the improvement of food security. The primary investigation comprises an assortment of articles spanning the years 2014 to 2023. After evaluating various search strategies, a total of 117 research publications were identified, of which 43 were pertinent. The article examines numerous developments in deep learning research. In addition, it will facilitate the assessment of the present and prospective state of plant disease research by employing deep learning methodologies.

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community.

SummaryThe earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization‐based deep learning (SSSO‐based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak‐based UNet (BLSNet), multilayer maxout, resistance spot welding‐based deep recurrent neural network (RSW‐based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

Highlights Generated custom imagery dataset using UAS, UGV, and handheld sensors separately in diseased corn fields. Proposed data pipeline utilizing Google Sheets API to establish communication between each platform and enable access through a web application. Developed deep learning-based disease management system for above and below-the-canopy corn disease diagnosis. Trained and evaluated disease detection models from each platform separately to provide management recommendations. Abstract. Early disease management following the onset of disease symptoms is crucial for controlling their spread. Heterogenous collaboration between unmanned aerial systems (UAS) and unmanned ground vehicles (UGV) for field scouting and disease diagnosis is a potential approach for developing automated disease management solutions. However, automation of crop-specific disease identification requires the use of above and below-canopy sensors and properly trained deep learning (DL) models. This research proposes to develop a novel disease management system for diagnosing corn diseases from above and below the canopy by collaboratively using edge devices mounted on UAS and UGV, respectively. Three separate datasets were acquired using UAS above the canopy, UGV below the canopy, and handheld imaging platforms within diseased corn fields. DL-based image classification models were first trained for identifying common corn diseases under field conditions, resulting in up to 95.04% testing accuracy using the DenseNet169 architecture. After creating bounding box annotations for disease images, You Only Look Once (YOLO)v7 DL-based object detection models were trained to identify diseases from each platform separately. Trained YOLOv7 models resulted in the highest mAP@IoU=0.5 of 37.6%, 46.4%, and 72.2% for locating and identifying diseases above the canopy using UAS, below the canopy using UGV, and handheld sensors, respectively. A client/server architecture was developed to establish communication between the UAS, UGV, and Google Spreadsheets via Wi-Fi communication protocol. The coordinates of diseased regions and distinct disease types were recorded using the client/server architecture on Google Spreadsheets. A web application utilized the data from the Google Spreadsheet to help users diagnose diseases in real time and provide recommendations for implementing appropriate disease management practices. This study reports findings of independently operated UAS and UGV that can potentially offer disease spread from below and over the corn canopy by combining the information. Keywords: Deep learning, Disease identification, Disease management, Object detection, UAS, UGV, YOLOv7.

Deep learning has emerged as a highly effective and precise method for classifying images. The presence of plant diseases poses a significant threat to food security. However, accurately identifying these diseases in plants is challenging due to limited infrastructure and techniques. Fortunately, the recent advancements in deep learning within the field of computer vision have opened up new possibilities for diagnosing plant pathology. Detecting plant diseases at an early stage is crucial, and this research paper proposes a deep convolutional neural network model that can rapidly and accurately identify plant diseases. Given the minimal variation in image texture and color, deep learning techniques are essential for robust recognition. In this study, we introduce a deep, explainable neural architecture specifically designed for recognizing plant diseases. Fine-tuned deep convolutional neural network is designed by freezing the layers and adjusting the weights of learnable layers. By extracting deep features from a down sampled feature map of a fine-tuned neural network, we are able to classify these features using a customized K-Nearest Neighbors Algorithm. To train and validate our model, we utilize the largest standard plant village dataset, which consists of 38 classes. To evaluate the performance of our proposed system, we estimate specificity, sensitivity, accuracy, and AUC. The results demonstrate that our system achieves an impressive maximum validation accuracy of 99.95% and an AUC of 1, making it the most ideal and highest-performing approach compared to current state-of-the-art deep learning methods for automatically identifying plant diseases.

The agricultural sector is paramount in meeting global food demands, necessitating research efforts to enhance productivity, improve food quality, and optimize profitability. Central to this endeavour is equipping farmers with efficient and affordable information and control technologies. Plant disease identification is pivotal for effective disease management and enhancing product quality. Various image processing and soft computing methods are employed for the early detection and diagnosis of plant diseases. Fuzzy logic, adept at handling fuzzy image data, is extensively discussed in the paper concerning precision agriculture, highlighting its efficacy in addressing agricultural challenges. Farmers can improve the accuracy and efficiency of disease detection and management by employing fuzzy logic techniques in precision agriculture, leading to higher crop yields, reduced input costs, and sustainable agricultural practices. Utilizing fuzzy logic in precision agriculture for disease detection and management involves leveraging the flexibility and interpretability of fuzzy logic systems to handle the inherent uncertainties and imprecisions in agricultural data. This paper explores applying fuzzy logic techniques in precision agriculture for disease detection and management. We discuss the theoretical foundations of fuzzy logic and its practical implementation in agricultural systems. Various methodologies and strategies are examined, including fuzzy membership functions, rule-based systems, fuzzy inference systems, and data fusion techniques. Case studies and examples are provided to illustrate the effectiveness of fuzzy logic in disease detection and management. These include applications in crop monitoring using remote sensing data, dynamic thresholding for disease risk assessment, and feedback control systems for automated disease management.

Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named "PDSE-Lite" based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework's performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework's performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework's applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.

Plant diseases are one of the major causes of crop yield reduction worldwide. Early detection and effective management of plant diseases will ensure a sustainable food supply and reduce famine. In this project, we propose a Plant Disease Management System (PDMS) that uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies to diagnose and manage plant diseases. The proposed system consists of two main components: a plant disease diagnosis module and a disease management module. The plant disease diagnosis module uses AI algorithms to analyze images of plant leaves and identify the type of disease affecting the plant. The AI model is trained on a large dataset of plant images using deep learning techniques, such as Convolution Neural Networks (CNNs) and Transfer Learning.

<b><sc>Abstract.</sc></b> Management of tar spot disease in corn traditionally relied on manual field scouting and visual analysis since it was first detected in the United States in 2015. Recent studies have reported computer vision-based applications for diagnosing tar spot disease lesions. However, human raters outperformed traditional computer vision approaches. In the past five to six years, deep learning techniques have showed promising results for different precision agriculture applications including fruit detection and counting, disease identification in various crops, and weed detection. However, limited studies have been conducted for developing deep learning-based disease identification and severity estimation tools for tar spot in corn. Therefore, in this study, a custom handheld imagery dataset consisting of 455 images was acquired for the tar spot disease in a greenhouse with complex background conditions. It was used to train deep learning-based disease identification models. The dataset was enhanced by combining with a publicly available CD&S dataset consisting of images for other corn diseases, namely, Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS). Image classification models were first trained to identify tar spot, NLB, GLS, and NLS diseases on diseased corn foliage. To accurately locate and identify tar spot disease lesions, the YOLOv4 object detection model was then trained. In addition, semantic segmentation models were trained using the UNet architecture for leaf and lesion segmentation. After training the image classification models, the highest testing accuracy of 99.41% was achieved with the DenseNet169 model. For YOLOv4 object detection, the highest mAP of 41% was achieved for locating and identifying tar spot disease lesions. Finally, for UNet semantic segmentation, the mIoU for leaf and lesion segmentation were 0.80 and 0.28, respectively. Therefore, traditional color histogram thresholding was used for segmenting the tar spot lesions, along with deep learning techniques, to develop a novel severity estimation framework. After evaluating the three models, the image classification model was deployed on a web application. Additionally, the model was deployed for a smartphone application to enable real-time analysis. Overall, is this study, different deep learning models were trained to accurately identify tar spot disease and its severity using images acquired under complex background conditions. In addition, a disease diagnosis tool was developed to help farmers accurately diagnose tar spot disease of corn.

Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation. Additionally, deep learning has begun as a powerful technique for to detect diseases in crops using images. Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices. This paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. A complete dataset of infected cotton plants having diseases like Bacterial Blight, Target Spot, Powdery Mildew, Aphids, and Army Worm along with the healthy ones is used. After pre-processing the images of the dataset, their region of interest is obtained by applying feature extraction techniques such as the generation of the biggest contour, identification of extreme points, cropping of relevant regions, and segmenting the objects using adaptive thresholding. During experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. However, other models also show the good performance in terms of precision, recall, and F1 score, with high scores close to 0.98 or 1.00, except for VGG19. The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. This strategy can also help to improve agricultural practices by ensuring sustainable cotton farming and increased crop output.

Plant diseases are assumed to be one of the primary causes regulating food manufacturing and reducing deficits in crop yield, and it is crucial that plant diseases have rapid spotting and identification. Since the demand for food is rising quickly due to population growth, the main issue for any country is plant disease mechanization in agricultural science. Recent developments in deep learning techniques have found use in plant disease recognition, offering a robust tool with exceedingly precise results. Our main goal is to employ deep learning mechanisms to better understand diseases and how to identify them quickly. In this regard, we analyzed 94 publications chosen from the last 7 years (2016–2023) that show how CNN's philosophy has evolved over this period with various approaches to treating plant diseases. Furthermore, a full description of numerous crops, diseases connected to them, various datasets relating to plant diseases, existing CNN models, and customized CNN architectures is provided. The results of this state‐of‐the‐art review can be implemented to comprehend the cutting‐edge trends in the application of deep learning (CNNs) to detect plant diseases as well as pinpoint any research gaps that require the scientific community's attention.

Chapter 8 - Sustainable plant disease protection using machine learning and deep learning

Plant pathology is a field which deals with the analysis, diagnosis and treatment of diseases in plants. These days agriculture is the main source of income in the Indian economy as well as important for livelihood. Identification of diseases in plants and crops are quite difficult unless someone have great knowledge and experience. Diseases in plants might cause severe damage to whole crop that leads to loss of income for farmers and results in descend of revenue for agriculture in Indian economy, if not identified and controlled forefront. Early prediction can help this situation. This documentation represents the prediction of plant diseases using images of the leaves that are given as input by the user and predicts the type of disease. In background we have used convolution neural network algorithm followed by image classification and deep learning techniques. The accuracy is obtained with the help of confusion matrix. The algorithm is implemented using Python language which uses Flask as the micro web framework for graphical user interface (GUI). User gives the input image and the predicted disease is printed.