Identifying Plant Diseases Research Articles

Revolutionizing plant health with machine learning for disease detection and diagnosis

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

Fostering ayurvedic plant wellness: Innovative leaf disease detection using computer vision and machine learning

Ayurveda is a conventional medicinal approach that has its roots in India and has been used for hundreds of years. It is still popular today since it is entirely natural and free of side effects. Although ayurvedic medicines are made from natural botanical substances, their safety depends on the way they are administered, taking into account the needs of the individual and the specific disease states they are treating. Diseases that affect the plants' leaves are regarded to be one of the main reasons for a decline in the output of ayurvedic plants in India's agricultural, economic, cosmetic, and pharmaceutical sectors. A plant exhibits signs of plant diseases in diverse sections of the plant, however, leaves are the most frequently observed component for spotting an infection. The objectives of this work are to modernize and innovate an autonomous ayurvedic plant leaf disease detection system by examining the scientific basis of computer vision. Additionally, a full analysis of computer vision techniques such as image pre-processing, segmentation, and feature extraction is covered to detect defects in plant leaves. This work investigates cutting-edge machine learning methods for identifying plant diseases that employ a variety of computer vision techniques. Based on the review, the article addresses the challenges and offers recommendations for changes that could be made in the future.

Identifying Crop Distress and Stress-Induced Plant Diseases Using Hyper Spectral Image Analysis

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms—the highly randomized trees and the Random Woodland set of rules—to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

Convolutional neural network as an efficient alternative to supervised learners for modelling plant diseases

The high productivity of the agricultural sector is crucial to the Global economy, as well as the national economy. For instance, India increased its nation's GDP by a factor of 17% to 18% in the past few decades. In India, the farming sector is the primary source of income. Numerous insects and illnesses have an impact on plant development, amount, and quality of products. Therefore, it is crucial to find the diseases early in the plant's development. Image processing is used to identify plant diseases and pests. Artificial intelligence, machine learning, and visual analysis have all been used in the past few decades to find and diagnose plant diseases. These computerized techniques are excellent for quickly monitoring vast acreage. In this study, we used the foliage of potato plants to identify diseased leaves affected by early blight, as well as late blight. A Comparison is thus laid between the Convolutional Neural Network (CNN) Model for this purpose, and the standard Supervised Learning Classifiers, like k – Nearest Neighbors, Support Vector Machine, AdaBoost, etc. The comparison is subjected not only towards the traditional Metrics, like Accuracy, Precision, etc. but is expanded to temporal domains as well, that is the time taken to compute the results. Also, the training, and testing split used while incorporating the model is another deciding factor in its performance. Several such training–testing splits are considered for such, and thereby, the convolutional Neural Network Model is found to be better in performance than the Supervised Machine Learners irrespective of the Splitting Ratio of the Training, and the Testing Data.

MAFDE-DN4: Improved Few-shot plant disease classification method based on Deep Nearest Neighbor Neural Network

Deep learning-based methods for accurately identifying plant diseases can be effective in improving crop yields. However, the effectiveness of these methods heavily relies on the availability of large-scale manually labeled datasets, which present technical and economic challenges. Few-shot learning methods can be generalized to new categories with a small number of samples, which is very promising in the field of plant disease recognition despite the limited sample size. However, due to the complexity of real-life scenarios, distribution of disease leaves, and significant intra-class variability and inter-class similarity resulting from crop species and disease species, existing methods perform poorly in the field of plant disease recognition. To address the above problems, this paper proposes an improved multi-scale attention fusion with discriminative enhancement deep nearest neighbor neural network MAFDE-DN4 based on DN4. Our approach makes three main contributions. First, we have designed a bidirectional weighted feature fusion module (BWFM) to enhance the aggregation of fine-grained features and enhance the network’s representation of complex disease images. Second, to tackle the issue of sparse feature descriptors being vulnerable to irrelevant noise in small sample conditions, a episodic attention module (EA) has been developed to produce scene category-relevant attention maps. This effectively mitigates the influence of irrelevant background information. Finally, we introduce additional spacing between category margins to enhance the original softmax loss function, amplify the inter-class differences to reduce the intra-class distances, and add L2 regularization constraint terms to stabilize the training process. To simulate different real-world scenarios, we set up different dataset settings. Under the 1-shot task and the 5-shot task, our method achieves 57.5% and 81.41% accuracy under the within-domain strategy and 36.54% and 51.23% accuracy under the cross-domain strategy. The experimental results show that our method outperforms existing related work in the field of plant disease recognition, whether it is a dataset with a single background or a field dataset with a complex background in a real scenario. MAFDE-DN4 based on Few-shot learning requires substantially less data on new categories of plant diseases.

Multi-Class Classification and Transfer Learning for Rapid Disease Detection in Green Leafy Vegetables Using Convolutional Neural Networks

Plant diseases cause various pathogens, including bacteria, viruses, fungi, and protozoa. Plant diseases and pathogens have an impact on the entire crop and field area. Plant diseases can be identified by various techniques; however, traditional techniques are time-consuming and require more effort. Up-to-date machine learning based approaches are used to identify plant diseases based on the image data. This study investigated the application of convolutional neural network (CNN) models for the detection and classification of diseases affecting green leafy vegetables. Several state-of-the-art CNN architectures, including InceptionV3 and DenseNet121, were evaluated on an image dataset of diseased and healthy leaf samples. Preprocessing techniques such as scaling, cropping, grayscale conversion, and normalization were applied to enhance the input images. The CNN models demonstrated high diagnostic accuracy, with InceptionV3 and DenseNet121 exhibiting exceptional performance across multiple metrics like sensitivity, specificity, accuracy, recall, F-measure, and Matthews correlation coefficient (MCC).

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380–1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops’ health, thus minimizing crop loss and optimizing yields.

Machine Learning Innovations for Precise Plant Disease Detection: A Review

The sustainable agriculture practices demands new innovations identifying plant diseases and instead of crop disease detection and precision and efficacy. An extensive review of the literature found through PubMed searches indicates a gap in the present approaches, which highlights the need for sophisticated machine learning solutions in the field of plant pathology. This study involves a comprehensive review of relevant publications collected via PubMed searches. The methodology involves the analysis of machine learning algorithms, datasets utilized and techniques applied for plant disease detection. Special attention is given to recent advancements in the field, focusing on the development and optimization of models tailored for precise and reliable disease identification. The study reveals compelling results, underscoring the transformative impact of machine learning innovations on plant disease detection accuracy. Specific algorithms exhibit superior performance, with implications for widespread applications in precision agriculture. These outcomes not only enhance current disease identification capabilities but also lay the groundwork for future advancements in automated and high-precision plant pathology diagnostics. The integration of machine learning emerges as a pivotal force in reshaping the landscape of plant disease detection.

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

The most significant peril to food safety arises from plant diseases, capable of substantially diminishing both the quantity and quality of agricultural yields. Identifying these plant diseases stands out as the foremost challenge within the agricultural sector. Convolutional and deep neural networks prove effective in resolving image classification challenges within the realm of computer vision. Numerous Deep Neural Network(DNN)-based structures have been employed to diagnose plant diseases. Many DNN models in the field make use of various iterations of Dense and DenseNet layers in order to enhance the receptive field and capture intricate features within the data. However, it is important to note that such models often come with a significant computational burden and can introduce aliasing artifacts due to their complexity and resource-intensive nature. To overcome those limitations, we proposed a novel Residual Nested Dilated DenseNet based deep-learning (RNDDNet) model in this paper. Residual Nested Dilated DenseNet model residual connections are achieving the required receptive field, and their dilation factors are effective in extracting more features. The RNDDNet model exhibits the highest level of accuracy in identifying plant diseases. This research introduces a less computational cost and compact model to detect diseases in plant leaves. The proposed model functions to identify diseases, utilizing a dataset comprising 3,800 photographs of chilli leaves, categorized into six distinct classes: five disorder classes and one healthy chilli class. Through experimentation, the outcomes indicate that the suggested model achieves an accuracy of 98.09 %, along with a precision of 97 %, a recall of 97.25 %, and an F1 score of 97.25%. The presented approach demonstrates its superiority over existing methodologies.

Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy.

Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI

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.

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

Plant Disease Detection

Abstract: This project addresses the critical challenge of plant disease detection in India, a nation where agriculture supports nearly 70% of the population. Traditional procedures for identifying plant diseases are both labor-intensive and time-consuming. To enhance efficiency and accuracy, this project introduces an innovative approach that integrates machine learning and image processing techniques. By analyzing images of leaves, the system identifies diseases based on key factors such as leaf color, damage extent, area, and texture parameters. Unlike conventional methods that depend on visual inspections or chemical processes, this automated system provides a cost-effective and efficient solution for monitoring extensive crop fields. By leveraging statistical machine learning and advanced image processing algorithms, the proposed solution ensures precise disease detection with minimized computational complexity and reduced prediction time

PLANT DISEASE RECOGNITION USING VGG-16

Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning has proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Artificial intelligence adds time efficiency and the possibility of identifying plant diseases, in addition to monitoring and controlling the environmental conditions in farms. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the worth of machine learning for disease detection, we present a convolutional neural network VGG-16 model to detect plant diseases, to allow farmers to make timely actions with respect to treatment without further delay. To carry this out, 19 different classes of plants diseases were chosen, where 15,915 plant leaf images (both diseased and healthy leaves) were acquired from the Plant Village dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 95.2% with the testing loss being only 0.4418. The proposed model provides a clear direction toward a deep learning-based plant disease detection to apply on a large scale in future. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming. Keywords—Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.

Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review

Plant diseases can significantly reduce crop yield and product quality. Visual inspections of plants by human observers for disease identification are time-consuming, costly, and prone to error. Advances in artificial intelligence (AI) have created opportunities for the rapid diagnosis and non-destructive classification of plant pathogens. Several machine vision techniques have been developed to identify and classify plant diseases automatically based on the morphology of specific symptoms. The use of deep learning models has achieved acceptable disease classification results, but they require large datasets for training, which can be labor-intensive, time-consuming, and computationally costly This problem can be solved, to a point, by using data augmentation techniques and generative AI in order to increase the size of the datasets. Furthermore, a combination of deep feature extraction and classification by machine learning was used for accurate disease detection and classification. In some cases, traditional base classifiers trained with small datasets including basic shape, color, and texture features can be feasible for the efficient identification of plant diseases. The performance of such classifiers depends primarily on the features extracted from images; therefore, feature extraction plays a vital role in identifying diseases. Feature engineering, a process to identify the most relevant variables from raw data in order to develop an efficient predictive model, is explored in this paper.

Plant Disease Detection Using Deep Learning-Based Convolutional Neural Networks With Transfer Learning Algorithms

Plant diseases can have significant impacts on agricultural production, leading to significant losses in yield and quality. Early detection and diagnosis of crop diseases is essential for effective control and management. In this study, We present a novel approach for the automated detection of plant diseases using deep learning-based Convolutional Neural Networks (CNNs) coupled with Transfer Learning algorithms. Our study focuses on developing a robust and adaptable system capable of accurately identifying plant diseases from image data. By leveraging a comprehensive dataset comprising diverse plant species and disease types, our goal is to train the model to achieve high accuracy and real-time detection capabilities. The potential significance of this research lies in its potential to significantly improve agricultural practices by offering farmers a valuable tool for prompt disease diagnosis and management, ultimately leading to increased crop yields and sustainable farming practices. Keywords: CNN, Deep Learning, Plant Diseases detection, Disease Diagnosis, Agriculture.

EAMultiRes-DSPP: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease

EAMultiRes-DSPP: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease

Identification and Classification of Leaf Diseases using Deep Learning

For agriculture to be sustainable, it is essential to monitor a plant's health and look for diseases. It is quite challenging to manually monitor plant diseases. To improve the agriculture industry and the prosperity of our nation, plant disease must be effectively identified. Several diseases cause the plant's leaves to die. Farmers have a harder time seeing these diseases because it's difficult to care for those plants without knowing about them. The method described in this paper uses Tensor Flow to identify illnesses in plant leaf pictures. CNN training is used on the model proposed to automatically diagnose disease using the object detection API tensor flow. This data is acquired from multiple sources. In order to treat the sickness, the proposed effort will also identify the causes and manifestations of the illness. In the work proposed, to identify plant diseases, sophisticated deep machine learning models based on specific CNN topologies were developed using photos of healthy or diseased plants' leaves. In comparison to existing models, CNN's numerous layers offer a great rating and a 96 percent accuracy level.

Plant Leaf Disease Detection

Abstract: The research presents an automated vision a system that makes use of the processing of images techniques to identify plant diseases in agricultural contexts. In order to monitor vast crop fields and automatically identify disease symptoms as soon that they occur on plant leaves, research on automated identification of plant infections is crucial to the agricultural industry. This method uses segmentation, colour modifications, and masking of green pixels to classify data based on learning from some training examples of that category. The simulated outcome, in the end, demonstrates that the network classifier in use offers reduced training error and increased classification accuracy