Plant Disease Detection Research Articles

Potential innovations from the application of beneficial soil microbes to promote sustainable crop production

Crop productivity may be significantly inhibited by factors, such as increased temperature, soil erosion, pathogen and pest attacks, and drought and salt stresses, mostly resulting from global climate change. However, microorganisms that are found in the rhizosphere can aid in the mobilization of essential soil nutrients, facilitate plant growth, and reduce abiotic and biotic stresses of plants. Soil microbes accomplish these beneficial functions via several mechanisms. Here, an elaborate description of the molecular mechanisms of plant growth-promotion by soil microbes and the potential of these organisms to be used as biofertilizers and biopesticides to improve plant health is provided. In addition, the possible revolution that could be realized by the synergism of these beneficial microbes with nanotechnology is discussed. While the use of biofertilizers to enhance plant growth has been demonstrated to be a beneficial phenomenon, this approach has often failed to yield the desired result in field applications. However, identifying microbial species with beneficial attributes and combining them with nanotechnology tools like nanoencapsulation and biosensors could lead to the formulation of important agriproducts (nanobiopesticides and nanobiofertilizers) that will ensure sustained delivery of the agriproducts and facilitate early detection and proper management of plant pests and diseases. It is anticipated that precision farming will improve agricultural sustainability by increasing crop production for the steadily increasing world population. Keywords: biofertilizers, secondary metabolites, nanoencapsulation, quorum sensing, volatile organic compounds, sustainable agriculture.

A Novel Approach for Red Rot Disease Detection in Sugarcane Plants Using Transfer Learning Based VGG16 Model

Red rot in sugarcane is a devastating fungal disease caused by Colletotrichum falcatum. It leads to wilting, red discoloration, and yield losses, which can impact sugar production globally. Red rot hampers sugarcane production, causing yield reductions, and economic losses, and affecting the nation’s sugar industry demanding stringent disease management strategies. There is a need of a system that detects the red rot disease at an early stage before it spreads. Transfer learning enhances agricultural models, leveraging pre-trained data for improved crop predictions, resource optimization, and sustainability. In this article, we have proposed a transfer learning-based VGG16 model for the detection of red rot disease in sugarcane on a large dataset. The model achieves a remarkable 98.85% accuracy and an F1 score of 0.93 in early red rot detection in sugarcane. This work underscores the potency of leveraging re-trained models for crop disease identification, offering a promising avenue for proactive disease management. This research marks a substantial step towards enhancing crop yield and sustainability, presenting an accessible and impactful technological solution for early disease detection in sugarcane, ultimately benefiting agricultural practices.

A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

Raman Spectroscopy for Plant Disease Detection in Next-Generation Agriculture

The present review focuses on recent reports on the contribution of the Raman method in the development of digital agriculture, according to the premise of maximizing crops with a minimal impact of agriculture on the environment. The Raman method is an optically based spectrum technique that allows for the species-independent study of plant physiology as well as the real-time determination of key compounds in a non-destructive manner. The review focuses on scientific reports related to the possibility of using the Raman spectrometer to monitor the physiological state of plants and, in particular, to effectively diagnose biotic and abiotic stresses. This review primarily aims to draw attention to and raise awareness of the potential of Raman spectroscopy as a digital tool capable of bridging the gap between scientists’ detailed knowledge of plants grown under laboratory conditions and farmers’ work. The Raman spectrometer allows plant breeders to take appropriate measures in a well-defined area, which will reduce the territory occupied by biotic and abiotic stresses, thus increasing yields and improving their quality. Raman technology applied to modern agriculture can positively affect the accuracy and speed of crop quality assessments, contributing to food safety, productivity and economic profitability. Further research and analysis on cooperation between farmers and scientists is indispensable to increase the viability and availability of Raman spectrometers for as many farmers and investors as possible.

C-MAN: A Multi-attention Approach for Precise Plant Species Classification and Disease Detection Using Multi-scale, Channel-Wise, and Cross-Modal Attentions

C-MAN: A Multi-attention Approach for Precise Plant Species Classification and Disease Detection Using Multi-scale, Channel-Wise, and Cross-Modal Attentions

Automated Early Detection of Plant Diseases a Machine Learning Approach

This study introduces an innovative strategy for the early detection of plant diseases leveraging machine learning algorithms, a crucial step towards bolstering global food security. Despite the importance of early disease detection for mitigating crop losses, inadequate infrastructure often hinders swift and accurate identification. This paper addresses these challenges by utilizing the Random Forest algorithm to distinguish between healthy and diseased plant leaves using a specially curated dataset. Our proposed method involves comprehensive phases of dataset creation, feature extraction, classifier training, and subsequent classification. For the extraction of image features, the Histogram of Oriented Gradient (HOG) technique is utilized, offering a robust method for image analysis. Embracing the capabilities of publicly accessible, large-scale datasets, our method provides a scalable solution to plant disease detection. The empirical results demonstrate promising levels of accuracy in disease monitoring, presenting transformative potential for disease management practices in agriculture. Particularly in resourceconstrained settings, our machine learning approach paves the way for efficient and effective plant disease detection, carrying significant implications for future crop protection strategies.

Smart Agriculture: A Comprehensive Survey on IoT-Enabled Plant Disease Detection and Agricultural Automation

This research paper is dedicated to the comprehensive review and discussion of diverse techniques employed in plant disease detection within the realm of agriculture. Emphasizing notable contributions and showcasing innovative methodologies, the research work takes a critical turn to address the myriad issues and challenges intricately woven into the integration of IoT data analytics in agriculture. The paper meticulously unravels the complexities associated with plant disease detection in the era dominated by IoT and data analytics. Serving as more than just a repository of current methodologies and technologies, this work actively illuminates the challenges that await further exploration. The insights derived from this exploration will provide a substantial foundation for emerging researchers. By shedding light on the evolving landscape of plant disease detection and the nuances of IoT integration in agriculture, this paper empowers researchers to actively contribute to the resilience and sustainability of agricultural practices in the face of ongoing challenges.

Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses.

In the context of global population growth expected in the future, enhancing the agri-food yield is crucial. Plant diseases significantly impact crop production and food security. Modern microfluidics offers a compact and convenient approach for detecting these defects. Although this field is still in its infancy and few comprehensive reviews have explored this topic, practical research has great potential. This paper reviews the principles, materials, and applications of microfluidic technology for detecting plant diseases caused by various pathogens. Its performance in realizing the separation, enrichment, and detection of different pathogens is discussed in depth to shed light on its prospects. With its versatile design, microfluidics has been developed for rapid, sensitive, and low-cost monitoring of plant diseases. Incorporating modules for separation, preconcentration, amplification, and detection enables the early detection of trace amounts of pathogens, enhancing crop security. Coupling with imaging systems, smart and digital devices are increasingly being reported as advanced solutions.

Performance Evaluation of YOLO Models in Plant Disease Detection

Performance Evaluation of YOLO Models in Plant Disease Detection

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.

Automatic plant disease detection using computationally efficient convolutional neural network

Automatic plant disease detection using computationally efficient convolutional neural network

Portable hydrogel kit based on Michael addition reaction for (E)-2-hexenal gas detection

Plants exhibit rapid responses to biotic and abiotic stresses by releasing a range of volatile organic compounds (VOCs). Monitoring changes in these VOCs holds the potential for the early detection of plant diseases. This study proposes a method for identifying late blight in potatoes based on the detection of (E)-2-hexenal, one of the major VOC markers released during plant infection by Phytophthora infestans. By combining the Michael addition reaction with cysteine-mediated etching of aggregation-induced emission gold nanoclusters (Au NCs), we have developed a portable hydrogel kit for on-site detection of (E)-2-hexenal. The Michael addition reaction between (E)-2-hexenal and cysteine effectively alleviates the etching of cysteine-mediated Au NCs, leading to a distinct fluorescence color change in the Au NCs, enabling a detection limit of 0.61 ppm. Utilizing the superior loading and diffusion characteristics of the three-dimensional structure of agarose hydrogel, our sensor demonstrated exceptional performance in terms of sensitivity, selectivity, reaction time, and ease of use. Moreover, quantitative measurement of (E)-2-hexenal was made easier by using ImageJ software to transform fluorescent images from the hydrogel kit into digital data. Such method was effectively used for the early detection of potato late blight. This study presents a low-cost, portable fluorescent analytical tool, offering a new avenue for on-site detection of plant diseases.

Plant Disease Classification Using Image Processing Technique

Agriculture remains pivotal to our economy, with farming playing a central role in revenue generation. Challenges such as pests, plant diseases, and evolving climate patterns pose threats to crop yield and production. Addressing these challenges, timely and accurate detection of plant diseases emerges as imperative. Manual detection, however, remains resource-intensive and often lags. Addressing this gap, this project proposes an innovative image processing-based system for rapidly detecting plant diseases. The system proficiently identifies specific diseases by analyzing images of plant leaves against a curated dataset. The emphasis of this study was on three major diseases: Bacterial Blight (with an accuracy of 98.6%), Alternaria Alternata (98.5714%), and Cercospora Leaf Spot (97.5%). The compelling results underline the system's capacity to swiftly and effectively categorize diseases, offering monoculture farmers an indispensable tool for obtaining prompt, disease-specific insights.

A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation

Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants’ leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves.

Enhancing real-time instance segmentation for plant disease detection with improved YOLOv8-Seg algorithm

With widespread uses in areas as diverse as traffic analysis and medical imaging, picture segmentation is a basic problem in computer vision. Instance segmentation, which combines object recognition with segmentation, is a powerful tool for item identification and exact delineation. Using the Tomato Leaf disease dataset as an example, this research delves into the topic of segmentation training by capitalizing on the simplicity of enhanced YOLOv8-Seg models. Tomato leaf disease are the focus of this instance-segmentation dataset, which seeks to resolve the pressing problem of agricultural difficulties. One instance segmentation networks, YOLOv8n-Seg is presented and compared in this article for the purpose of Tomato leaf disease identification. The models are tested in difficult situations to see how well they can detect and separate garbage occurrences. Results show that enhanced YOLOv8-Seg is useful for agriculture by accurately segmenting instances of tomato leaf disease detection.

Isolation and Enzymatic Characterization of Fungal Strains from Grapevines with Grapevine Trunk Diseases Symptoms inCentral Mexico.

Grapevine production is economically indispensable for the global wine industry. Currently, Mexico cultivates grapevines across approximately 28 500 hectares, ranking as the 26th largest producer worldwide. Given its significance, early detection of plant diseases' causal agents is crucial for preventing outbreaks. Consequently, our study aimed to identify fungal strains in grapevines exhibiting trunk disease symptoms and assess their enzymatic capabilities as indicators of their phytopathogenic potential. We collected plant cultivars, including Malbec, Shiraz, and Tempranillo, from Querétaro, Mexico. In the laboratory, we superficially removed the plant bark to prevent external contamination. Subsequently, the sample was superficially disinfected, and sawdust was generated from the symptomatic tissue. Cultivable fungal strains were isolated using aseptic techniques from the recovered sawdust. Colonies were grown on PDA and identified through a combination of microscopy and DNA-sequencing of the ITS and LSU nrDNA regions, coupled with a BLASTn search in the GenBank database. We evaluated the strains' qualitative ability to degrade cellulose, starch, and lignin using specific media and stains. Using culture morphology and DNA-sequencing, 13 species in seven genera were determined: Acremonium, Aspergillus, Cladosporium, Dydimella, Fusarium, Sarocladium, and Quambalaria. Some isolated strains were able to degrade cellulose or lignin, or starch. These results constitute the first report of these species community in the Americas. Using culture-dependent and DNA-sequencing tools allows the detection of fungal strains to continue monitoring for early prevention of the GTD.

Crop Disease Prediction Using Web Application

Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a plant and cause farmer to lose a substantial sum yearly. Deep Learning (DL) can play a crucial role in helping farmers to prevent crop failure by early disease detection in plant leaves. In the experiment, examination conducted by Convolution Neural Network (CNN) model on dataset (which consists of images of healthy and infected leaves) to detect plant diseases and Web Application is used for real-life crop disease prediction. The proposed Web Application aims to assist farmers in detection of plant diseases by analyzing images of the plant leaves. The proposed application uses the CNN model to distinguish healthy and infected leaves. The goal is to help farmers and prevent economic loss by detecting plant diseases early

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

Leaf Disease Detection Using Convolutional Neural Network

Abstract: The aim of the project is to develop a Leaf disease detection system using deep learning, specifically Convolutional Neural Networks (CNNs). The project focuses on classifying images of plant leaves into 39 different disease categories by utilizing deep learning, specifically Convolutional Neural Networks (CNNs), for plant disease detection based on leaf images. The dataset consists of 39 classes with a totalof 61,486 images, and various augmentation techniques are applied to increase dataset size. The implementation involves PyTorch, with transformations for data augmentation, dataset creation usingImage Folder, and a split into training, validation, and testing sets. The CNN model is designed for image classification, using ReLU activation and soft max for the final layer. The training process involves batch gradient descent, and the model achieves an accuracy of 87% on training data, 84% on validation data, and 83% on test data. The key objectives include utilizing image data, implementing data augmentation techniques, creating a dataset, and training a CNN model to accurately predict and classify plant diseases based on input images. The ultimate goal is to provide a tool that can assist in early detection and diagnosis of plant diseases through automated analysis of leaf images

Botanic Shield: A Deep Learning Solution for Early Detection of Plant Disease

Abstract: Plant diseases threaten both food security and the botanical system of the environment which canlead to low- grade food quality and life style. Early detection of plants diseases is the key feature in plantproduction which increase the better growth of plant, results in best yield and better food quality andquantity. The plant condition can be determined byusing the photos of the plant which will be in the formof jpg using digital image processing and the current emerging technology Deep Learning. DeepLearning has made breakthroughs in the field of digital image processing, far superior to traditional methods. The Digital image processing process happens through convolution neural network (CNN) and computer vision techniques, the image recognition technology based on deep learning does not need to extractspecificfeatures, and only through iterative learning can find appropriate features, which can acquire global andcontextual features of images, and has strong robustness and higher recognition accuracy. By inputting a test image into the classification network, the network analyses the input image and returns a label that classifiesthe image. The added feature is thatit also suggeststhe required pesticides after the detection