Identification Of Plant Diseases Research Articles

Harnessing Ai for Socio-economic Equity in Uganda: Bridging the Digital Divide through Agricultural Innovation

The paper explores the application of artificial intelligence (AI) technologies in addressing digital divide challenges and enhancing socio-economic equity in Uganda. The digital divide, characterized by disparities in access to digital resources, internet connectivity, and digital literacy, significantly impacts socio-economic development. This research focuses on AI technologies such as machine learning, image recognition, natural language processing (NLP), and GIS-integrated AI, which are mildly actively used in Uganda’s agricultural sector. Studies have shown that digital technologies can address key challenges in agriculture, such as access to extension services, marketing systems, and financial inclusion. For example, mobile money has significantly increased financial inclusion among Uganda's unbanked populations, including women, youth, and rural households. However, barriers such as low digital literacy, limited smartphone ownership, and inadequate internet access hinder the adoption of these technologies, especially among disadvantaged groups. Therefore, while digital innovations have the potential to improve productivity and inclusiveness in agriculture, their limited use could exacerbate existing inequalities by leaving behind those who cannot easily access or afford these technologies. Leveraging these technologies, the study examines how they contribute to improving crop yield predictions, pest and disease management, and precision agriculture. Through the analysis of initiatives undertaken by the National Agricultural Research Organization (NARO), including the use of AI-driven image recognition for plant disease identification and NLP for farmer support systems, highlighting the practical applications and benefits. The findings emphasize the critical infrastructure requirements, ethical considerations, and potential challenges in deploying AI solutions in low-resource communities [9]. Ultimately this research aims to provide a comprehensive framework for utilizing AI to foster digital inclusivity and promote socio-economic growth in developing regions, using Uganda as a case study

LeafNST: an improved data augmentation method for classification of plant disease using object-based neural style transfer

Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through computer vision techniques have become imperative for timely intervention. However, popular plant disease datasets often suffer from data imbalance, with certain classes underrepresented, hindering the performance of machine learning models. Traditional data augmentation methods, such as rotation and flipping, are limited in their effectiveness, especially when faced with imbalanced datasets. To address this limitation, we explore advanced data augmentation techniques, including Generative Adversarial Networks (GANs) such as CycleGAN and LeafGAN, which have shown promise in generating synthetic images. However, we propose an innovative approach of Object-based single Style Transfer on a single neural network for augmenting the plant disease dataset. This technique focuses on mitigating data imbalance issues within datasets, which can adversely affect the model’s ability to generalize across diverse classes. The proposed method is compared with state-of-the-art data augmentation techniques, highlighting its superiority in addressing data imbalance issues. Our approach aims to produce more realistic and diverse synthetic images, leading to improved model generalization and accuracy in plant disease classification tasks validated using different classifiers. The efficiency of our approach is validated through extensive experimentation and benchmarking against existing methods.

Receptonics-based real-time monitoring of bacterial volatiles for onsite fire blight diagnosis

Fire blight, caused by the bacterium Erwinia amylovora, is a rapidly progressing and highly contagious disease of apple and pear, which leads to great economic losses with high recovery fees. To control fire blight, the infected trees are either treated with antibiotics or, in some countries, buried. To minimize the damage caused by E. amylovora and to manage its spread to other trees and orchards, early diagnosis of fire blight is critical. The direct extraction of E. amylovora cells or DNA from infected tissues is a laborious and time-consuming process, and a non-destructive diagnosis method has not yet been applied. In this study, we employed receptonics, a nanotechnology platform equipped with a receptor protein that perceives airborne bacterial signals and converts them into electronic signals, for onsite fire blight diagnosis. Two bacterial volatiles, 2,3-butanediol and 2-phenylethyl alcohol, were used as E. amylovora biomarkers both in vitro and in situ. To design receptor protein for identifying the target biomarkers, the bioprobe, nanodiscs human olfactory receptor family 1 subfamily D member 5 (hOR1D5), and Drosophila melanogaster or9a were engineered and fabricated. Our portable receptonics platform was able to detect both bacterial volatiles at concentrations as low as 10 pM within 10 s under field conditions. Thus, our results suggest that the receptonics platform could be used as a non-destructive diagnostic tool for the onsite, rapid, and accurate identification of plant diseases.

A Multitask Learning-Based Vision Transformer for Plant Disease Localization and Classification

Plant disease detection is a critical task in agriculture, essential for ensuring crop health and productivity. Traditional methods in this context are often labor-intensive and prone to errors, highlighting the need for automated solutions. While computer vision-based solutions have been successfully deployed in recent years for plant disease identification and localization tasks, these often operate independently, leading to suboptimal performance. It is essential to develop an integrated solution combining these two tasks for improved efficiency and accuracy. This research proposes the innovative Plant Disease Localization and Classification model based on Vision Transformer (PDLC-ViT), which integrates co-scale, co-attention, and cross-attention mechanisms and a ViT, within a Multi-Task Learning (MTL) framework. The model was trained and evaluated on the Plant Village dataset. Key hyperparameters, including learning rate, batch size, dropout ratio, and regularization factor, were optimized through a thorough grid search. Early stopping based on validation loss was employed to prevent overfitting. The PDLC-ViT model demonstrated significant improvements in plant disease localization and classification tasks. The integration of co-scale, co-attention, and cross-attention mechanisms allowed the model to capture multi-scale dependencies and enhance feature learning, leading to superior performance compared to existing models. The PDLC-ViT model evaluated on two public datasets achieved an accuracy of 99.97%, a Mean Average Precision (MAP) of 99.18%, and a Mean Average Recall (MAR) of 99.11%. These results underscore the model's exceptional precision and recall, highlighting its robustness and reliability in detecting and classifying plant diseases. The PDLC-ViT model sets a new benchmark in plant disease detection, offering a reliable and advanced tool for agricultural applications. Its ability to integrate localization and classification tasks within an MTL framework promotes timely and accurate disease management, contributing to sustainable agriculture and food security.

Lightweight convolutional neural network-based plant disease identification for protection and landscape design

Plant diseases significantly impact landscape design, necessitating comprehensive consideration and effective management measures to ensure the health, aesthetics, and sustainability of landscapes. Early detection and timely control of plant diseases are crucial, but traditional monitoring methods, which rely on manual observation and sample collection, are inadequate for covering large garden areas and may delay necessary treatments. This study addresses these challenges by constructing a small Rosa chinensis disease dataset through field collection and data augmentation techniques. We propose MixResCoAtNet, an improved model based on the lightweight MixNet framework, to identify and categorize diseases from plant leaf images using convolutional neural networks (CNNs). Comparison experiments with various classical convolutional network models demonstrate that MixResCoAtNet outperforms these models, offering more competitive performance. Additionally, due to its lighter structure, MixResCoAtNet shows greater potential for deployment on mobile devices, facilitating real-time and efficient plant disease monitoring and management in landscape design.

From leaf to harvest: achieving sustainable agriculture through advanced disease prediction with DBN-EKELM.

In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. The objective of this study is to develop a new deep learning-based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network-based enhanced kernel extreme learning machine (DBN-EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN-EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge-based optimization algorithm (NBGSK). The efficacy of the proposed DBN-EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F-measure. Experimental analyses demonstrate that the DBN-EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry.

Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications

This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research.

Plant pathology identification using local-global feature level based on transformer

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.

Identification of Rice Plant Diseases Using Convolutional Neural Network Method

Identification of Rice Plant Diseases Using Convolutional Neural Network Method

Plant disease identification and pesticides recommendation using Dense Net

Plant disease identification and pesticides recommendation using Dense Net

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.

Detection and Classification on Plant Disease using Deep Learning Techniques

Plant diseases are a major problem for the agriculture industry because they can cause large crop losses and jeopardize food security. Deep learning approaches have demonstrated encouraging results in automating plant disease diagnosis and detection in recent years. In the context of plant disease diagnosis, this study examines the efficacy of two well-known convolutional neural network architectures: DenseNet121 and VGG16. Plant Village datasets are used for pretrained and fine-tuning of the DenseNet121 and VGG16 architectures, respectively. The dataset includes Images of both healthy and sick plants. To guarantee the models' resilience and generalizability, the dataset include 15 different classes and 4 types of plants namely Tomato, Potato and Pepper Bell. We compare the accuracy, precision, recall, and F1-score of DenseNet121 and VGG16 for plant disease classification through extensive testing and analysis. To determine if they are practically feasible for use in real-world applications, we also examine their model complexity and computing efficiency. Our findings show that DenseNet121 and VGG16 can both correctly diagnose plant diseases in a variety of species. Although DenseNet121 outperforms VGG16 in terms of overall accuracy and computational efficiency, both models obtain high accuracy rates. Additionally, DenseNet121 has superior generalization performance, especially in identifying uncommon or underrepresented illness classes. All things considered, this work emphasizes the promise of deep learning models-DenseNet121 in particular-as useful instruments for automated plant disease identification and points to directions for further investigation to improve the efficiency and scalability of such systems for real-world use in agriculture.

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.

Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-Decoder Cascaded Network (DEDCNet) model can precisely segment diseased areas from the leaf images to differentiate and classify multiple diseases. On the other hand, the model training depends on the appropriate selection of hyperparameters. Also, this network structure has weak robustness with different parameters. Hence, in this manuscript, an Optimized DEDCNet (ODEDCNet) model is proposed for improved leaf disease image segmentation. To choose the best DEDCNet hyperparameters, a brand-new Dingo Optimization Algorithm (DOA) is included in this model. The DOA depends on the foraging nature of dingoes, which comprises exploration and exploitation phases. In exploration, it attains many predictable decisions in the search area, whereas exploitation enables exploring the best decisions in a provided area. The segmentation accuracy is used as the fitness value of each dingo for hyperparameter selection. By configuring the chosen hyperparameters, the DEDCNet is trained to segment the leaf disease regions. The segmented images are further given to the pre-trained Convolutional Neural Networks (CNNs) followed by the Support Vector Machine (SVM) for classifying leaf diseases. ODEDCNet performs exceptionally well on the PlantVillage and Betel Leaf Image datasets, attaining an astounding 97.33% accuracy on the former and 97.42% accuracy on the latter. Both datasets achieve noteworthy recall, F-score, Dice coefficient, and precision values: the Betel Leaf Image dataset shows values of 97.4%, 97.29%, 97.35%, and 0.9897; the PlantVillage dataset shows values of 97.5%, 97.42%, 97.46%, and 0.9901, all completed in remarkably short processing times of 0.07 and 0.06 seconds, respectively. The achieved outcomes are evaluated with the contemporary optimization algorithms using the considered datasets to comprehend the efficiency of DOA.

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.

Automated Plant Disease Detection Using Deep Learning

Traditional methods of diagnosing plant diseases are mainly based on expert diagnosis which easily causes delay in crop disease control and crop management. Due to the problems of many target areas and similar target types in the process of plant disease detection, the identification accuracy and speed are required to be high. Therefore, it is necessary to optimize and improve the existing methods (CNN, RCNN, Fast RCNN, Faster RCNN, and SSD) to meet the detection needs. So, we came up with a deep learning-based approach to identify the plant leaf diseases and classify the diseases using an object detection model called YOLO. There are different versions of YOLO, proposed in recent times, in that YOLOv5 model is considered one of the best models and the other is the recent version, YOLOv8 which was proposed in 2022. So, in this paper we compared the two models of YOLO, YOLOv5 and YOLOv8 on the same dataset, and found that for the dataset used the YOLOv5 model was found to be the better model with a mAP of 0.641, while the YOLOv8 model has mAP of 0.516. This study proposes that the YOLOv5 model is suitable for plant disease identification tasks by comparing it with the latest version of the YOLOv8 model which was proposed in 2022. To make the YOLO model to be better it can be optimized and the transfer learning ability of the model can be used to expand the application scope in the future. Keywords— Deep Learning, CNN, RCNN, Fast RCNN, Faster RCNN, SSD, YOLO, mAP, Transfer Learning, Disease Classification, Detection.

Identification of potato plant diseases using CNN model

Identification of potato plant diseases using CNN model

Identification of Plant Diseases Using Imaging Processing and Machine Learning 1st

This comprehensive review explores the most current developments in the use of image processing and machine learning methods for the diagnosis of plant diseases. After a thorough review of the literature, we critically assess the different frameworks and methods used to classify disorders affecting plants. Our study highlights the advantages and disadvantages of each approach while focusing on how accurate it is in diagnosing a wide variety of illnesses. In addition, we investigate new developments and trends in this quickly developing industry. We conclude by talking about the ongoing difficulties and suggesting some directions for further study to improve the effectiveness of plant disease detection systems. Keywords— CNN, VGG16, image processing, classification, neural networks, and machine learning

AgriSolutions: Innovations in Crop Disease Prediction, Crop Recommendation, and E Commerce

Abstract: Crop disease prediction and crop recommendation are important steps towards sustainable agriculture. The use of deep learning, EfficientNet_v2 and image processing technologies can help in improve accuracy in these tasks. Identification of plant diseases and recommendation of crops cannot maximize only yield production but can be supportive for varied types of agricultural practices The existing system of crop recommendation employs data analysis to suggest suitable crops based on soil and climate factors, while disease prediction system utilizes predictive models to anticipate and prevent crop diseases. In this Paper, we propose a model that combines these techniques to accurately identify to recommend suitable crops based on the Soil Parameters and climate conditions of the farm. Our proposed model achieved and classify plant diseases, recommend suitable crops based on the soil condition. To achieve this, we trained a deep neural network on a large dataset of images of healthy and diseased plants, and performed image preprocessing.

PlantGuard: Leaf Disease Detecting and Suggests Remedy using Machine Learning

Abstract: Plantguard is a leader in the fusion of artificial intelligence and agriculture, with a primary focus on transforming cropmanagement via sophisticated leaf disease detection. The initiative provides quick and reliable plant disease identification from leaf photos by utilizing cutting-edge computer vision and machine learning algorithms, giving farmers the ability to take prompt action. Plantguard is dedicated to creating user-friendly interfaces that will allow precision agriculture to be smoothly incorporatedinto conventional agricultural methods. This will promote sustainable production, boost farmer productivity, and raise their financial stability. This novel strategy represents a significant step toward a future for global agriculture that is both resilient and more technologically advanced. Convolution neural network (CNN), transfer learning, deep learning, image processing, data augmentation, and agricultural applications are some of the index terms

PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection

Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.