A novel deep learning method for detection and classification of plant diseases

The wide scale prevalence of diseases in agricultural crops affects both the production quality and quantity of agricultural products at local to regional scale. More often than not, the diseases remain unidentified causing huge distress to the farmers while threatening national food security. In order to circumvent this problem, early diagnosis of diseases using a fast and reliable method is beneficial. Plant disease identification from images captured by digital cameras is an area of active research. Use of various machine learning algorithms for plant disease classification and the evolution of deep convolutional neural network (CNN) based architectures have further enhanced the plant disease classification accuracy. In this context, an automated computer vision-based plant disease detection and classification scheme from plant and leaf’s photographs will be highly desirable. Although, there exist a few techniques currently used in an adhoc fashion for plant disease detection and/or classification, a systematic study to evaluate their usage and efficacy on actual plant data has largely remained unexplored.The aim of this paper is to evaluate various CNN based state-of-the-art transfer learning architectures like GoogLeNet, AlexNet, VGG16 and ResNet50V2 models for plant disease detection and classification. The models were tested on popular publicly available three plant disease benchmark database such as PlantVillage Dataset, New Plant Disease Dataset and Plant Pathology Dataset. Various validation metrics such as Precision, Recall, F1 score and overall accuracy were used to evaluate the final results of the experiments, which revealed that VGG16 rendered highest accuracy of 96.6%, 98.5% and 89% on the three dataset respectively, outperforming all other state-of-the-art models.

Plant diseases recognition on images using convolutional neural networks: A systematic review

With the rapid development of precision agriculture and smart agriculture, the need to build an automatic identification and detection system for diseases and insect pests is increasing. Using computers to correctly label plant diseases and insect pests is an important prerequisite for achieving accurate classification of plant diseases and insect pests and ensuring system performance. In order to improve the accuracy of computer classification of plant pests and diseases, this paper proposes an automatic pest identification method based on the Vision Transformer (ViT). In order to avoid training overfitting, the plant diseases and insect pests data sets are enhanced by methods such as Histogram Equalization, Laplacian, Gamma Transformation, CLAHE, Retinex-SSR, and Retinex-MSR. Then use the enhanced data set to train the constructed ViT neural network, so as to realize the automatic classification of plant diseases and insect pests. The simulation results show that the constructed ViT network has a test recognition accuracy rate of 96.71% on the plant disease and insect pest public data set Plant_Village, which is about 1.00% higher than the Plant disease and pest identification method based on traditional convolutional neural networks such as GoogleNet and EfficentNetV2.

The aim of this article to introduces various image processing and machine learning techniques used to identify plant diseases based on images of diseased plants in order to recognize disease in plants from images and necessary in image processing and machine learning as they apply to the identification and categorization of plant diseases. We meticulously review more content and provide important standards. These characteristics include things like the size of the photo collection, preprocessing, segmentation techniques, classifier types, classifier resolution, and other things. To suggest and arrange our work on the classification and identification of plant diseases, we explain our study here. Then, based on the principal technical solution used in the method, each of these groups is split using machine learning techniques. Photos of plant disease samples were processed using support vector (SVM) and k-mean clustering techniques to extract color and texture information. The results show that the SVM classifier is a very good tool for detecting and identifying plant-borne diseases in agricultural crops.

Plant recognition and diseases identification have an impact on the sustainable development of many countries in theagricultural sector. The automatic plant recognition and diseases identification will assist the specialists and expertsin agriculture to overcome many of plant diseases and problems. The automation of plant diseases identification andrecognition approaches have received considerable interest in the last years because their effect on the growth of theeconomy of countries, which may depend mainly on agriculture and to reduce the economic losses in the sustainableagriculture industry in general. However, human cognition and sight are not sufficient to identify the region of interestin the images of plants, usually, stems and leaves. Nowadays, image-based methods are considered as a visual assistingof plant recognition and diseases identification with the aid of the recent advances in image processing area. In thispaper, we describe and analyze the automated image-based methods and discuss the state-of-art of plant recognitionand diseases identification that has been applied in the last years. Also, we explore the role of image processingmethods and classifiers in plant diseases identification and recognition. Different types of datasets of plant diseasesidentification and recognition are introduced briefly with their existing problems. As an example, the preprocessingphase of this issue is implemented based on real infected tomato leaves. Also, shape feature, color feature, and texturefeature have been reviewed. Moreover, we described the important classifiers that are used currently used in theclassification process. Also, hybrid classifiers can integrate the results from multiple algorithms with the aim ofimproving classification accuracy. Therefore, some of the well-known hybrid classifiers for plant diseasesidentification and recognition have been presented. Some solutions of using image-based methods such as complexbackgrounds of the region of interest, different plant diseases can produce similar symptoms, and the conditions ofcapturing images have been presented. Finally, some points of the future work are proposed.

Plant diseases are responsible for economic loss in agricultural industry, as they destroy the crops. Although pesticides have been used to modify crop production, the excess amount of use of pesticides affects the environment negatively. Hence detection of diseases and differentiating it from nutritional deficiency has a considerable impact in deciding the requirement of pesticides. The conventional methods for plant diseases identification involve tedious chemical processes to be carried out in the laboratory and is also time consuming. This paper presents an automated system for identification and classification of plant diseases using Machine Learning (ML) and image processing technique. The feature extraction method is applied on images for training the algorithm. The skill of different Machine Learning algorithms is evaluated using the training data to find the best suiting algorithm for disease identification. The test folder contains the unseen images and is used for validating the performance of system in identification of plant diseases. The overall accuracy of the system is 95 percent. The system can be trained with large amount of images and yields accurate results at a faster rate.

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.

A review of imaging techniques for plant disease detection

The loss of agricultural yields is often attributed to plant diseases that affect crops.Early detection and effective control of these diseases remain a major challenge for farmers, leading to substantial yield losses that pose a threat to a growing population. This study aims to identify gaps in the use of deep learning for early detection and classification of plant diseases. Deep learning techniques can help farmers quickly identify diseases, thereby increasing agricultural productivity. Better early detection can help developing countries mitigate food insecurity and economic challenges. The results show that despite promising progress in plant disease identification, several challenges and limitations persist in the literature over the past five years and deserve to be addressed. A critical analysis was conducted to identify the limitations and challenges of existing solutions to serve as a basis for future research on the improvement, early identification and classification crop diseases.

Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. An enhanced convolutional neural network (CNN) along with long short-term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre-trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM-CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. The presented model attained 99.2% accuracy compared to the cutting-edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre-trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

This survey concludes that CNN-based deep learning models offer opportunities for early and accurate plant disease detection, supporting sustainable agriculture while acknowledging potential challenges in practical real-world application. Deep learning (DL) methods have transformed image-based plant disease diagnosis by addressing complex challenges specific to crop health monitoring in agriculture. The automated identification and classification of plant disease from images have significant interest, which can be expected to increase crop health monitoring and agricultural productivity. Yet, notwithstanding these benefits, image-based identification of plant diseases is a sophisticated challenge. Proper identification of certain plant varieties and proper determination of disease manifestations are key factors in the administration of effective care and sustainable management of disease. In this research paper an extensive overview Convolutional Neural Networks (CNNs) is implemented using deep learning method for disease detection in plants. This article focuses particularly on highlighting recent research achievements by the last half-decade emphasizing CNN-based models constructed for detecting plant leaf disease. The survey delves into key innovations, methods, and issues faced with the application of CNNs to monitor plant health. Specifically, it highlights the manner in which deep convolutional neural networks (DCNNs), learned using large-scale image databases, are becoming effective means of early and precise detection of plant diseases. Lastly, this paper charts exciting future directions for DL-aided plant disease diagnosis, while providing a balanced critique of the potential, as well as the limitations of CNNs in practical agricultural contexts.

The detection and classification of plant diseases are crucial to ensuring food security and maximizing agricultural productivity. Traditional methods of plant disease identification are time-consuming and labor-intensive, necessitating the adoption of more efficient and accurate techniques. In recent years, advancements in machine learning have led to the development of robust methods for automated plant disease detection. This research paper presents a novel approach for plantdisease detection using Convolutional Neural Networks (CNNs). CNNs have demonstrated exceptional performance in image recognition tasks, making them a promising choice for detecting diseases in plant images. The proposed system utilizes a pre- processed dataset of plant images, comprising both healthy and diseased samples, to train the CNN model. Keywords—Plant disease and detection, convolutional neural networks, deep learning architectures, feature extraction, classifier methods

One of the primary needs of humans is food, which can be obtained through farming. Not only does agriculture meet the necessities of humankind, but it is also a primary source of employment. Agriculture is the main driver of employment and economic growth for a growing country such as India. For a thriving agricultural and economic sector, plant disease identification is a crucial concern to enhance productivity requires attention. Conventional approaches for detecting plant diseases are gruelling, time-consuming and demand a great deal of experience. Plant disease can be detected in a timely manner as it emerges on plant leaves, with the utilization of numerous machine learning and deep learning approaches. These high precision and time-efficient techniques are the way forward to generate qualitative farm produce. The current article examined a few methods currently used for analysing data sources, feature extraction, data augmentation and classification of plant diseases. PRISMA guidelines have been utilized to select the articles, using various keywords from peer-reviewed articles published in several databases between 2016 and 2025. Following the removal of studies based on the abstract, title, full text and conclusion, 75 publications were found and examined for their immediate relevance to plant disease recognition and categorization. Of these, 45 publications have been selected for this systematic review.

The identification and classification of plant diseases is of great significance to ecological protection and deep learning methods have made a great of progress in the common plant diseases identification for specific plant. While faced with the same plant disease of other plants, due to the insufficient or low quality training data, current deep learning methods will be difficult to identify the diseases effectively and accurately. Inspired by the advantages of GAN in dataset expansion, we propose the CycleGAN based confusion model in this paper. In this paper, GAN framework is improved by adding noise label and learn together during training stage, which migrates the data of common plant diseases to the plants with insufficient or low quality data. In order to evaluate the quality of the migrated training dataset among different GAN approaches, we introduce the quality indicators of the migration images such as MMD, FID, EMD etc. We compare our model with other GANs model, and the experimental results show that the proposed model obtains better results in the migration process, which make it more effective for the identification of cross species plant diseases.

To identify the recent advancements in the development of plant disease detection and classification system based on Machine Learning (ML) and Deep Learning (DL) models. In this study, we have collected more than 45 papers published during the year 2017-2020 from the peer-reviewed journals of different databases such as Scopus and Web of Science analogous to the keywords such as plant disease identification, recognition, and classification using ML and DL algorithms. An organized way of analysis of various plant disease classification models has been shown in well-formed tables. In this paper, we have conducted a systematic literature study on the applications of the state-of-the-art ML and DL algorithms such as Support Vector Machine (SVM), Neural Network (NN), K-Nearest Neighbor (KNN), Naïve Bayes (NB), other few popular ML algorithms and AlexNet, GoogLeNet, VGGNet, and other few popular DL algorithms respectively for plant disease categorization. Each stated algorithm is characterized through the corresponding processing methods such as image segmentation, feature extraction, along with the standardized experimental-setup metrics such as total number of training/testing dataset employed, number of diseases under considerations, type of classifier utilized, and the percentage of classification accuracy. This work will be a beneficial resource for researchers to recognize any particular type of plant diseases through data-driven approaches. The development of mobile-based applications using the studied ML/DL approaches will surely increase agricultural productivity.