scholarly journals Detection of soilborne disease utilizing sensor technologies: Lessons learned from studies on stem rot of peanut

2021 ◽  
Author(s):  
Xing Wei ◽  
Marcela Aguilera ◽  
Rachael Walcheck ◽  
Dorothea Tholl ◽  
Song Li ◽  
...  
Keyword(s):  
Crop Production ◽  
Plant Disease ◽  
Lessons Learned ◽  
Plant Diseases ◽  
Stem Rot ◽  
Disease Detection ◽  
Soilborne Disease ◽  
Current State ◽  
Major Constraint ◽  
Crop Disease

Soilborne plant diseases are a major constraint to crop production worldwide. Effective and economical management of these diseases is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Sensor-based technologies are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of our research is to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies that can be utilized for the detection of soilborne plant diseases. Here we summarize the current state of sensor-based technologies for plant disease detection and provide examples from our own research that illustrate the advantages and limitations of different sensor-based methods for detecting soilborne diseases. In addition, the potential to adapt different sensor-based technologies to practical use in the field is discussed.

scholarly journals A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

2022 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Folasade Isinkaye
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Plant Disease ◽  
Recommendation System ◽  
Research Effort ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Treatment Recommendation ◽  
Disease Detection ◽  
Recommended Treatment

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

scholarly journals Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

scholarly journals Prospects for Biological Soilborne Disease Control: Application of Indigenous Versus Synthetic Microbiomes

2017 ◽  
Vol 107 (3) ◽  
pp. 256-263 ◽  
Author(s):  
Mark Mazzola ◽  
Shiri Freilich
Keyword(s):  
Disease Control ◽  
Crop Production ◽  
Production Systems ◽  
Agricultural Soils ◽  
Management Strategies ◽  
Plant Diseases ◽  
Disease Suppression ◽  
Rapid Screening ◽  
Soilborne Disease ◽  
Functional Attributes

Biological disease control of soilborne plant diseases has traditionally employed the biopesticide approach whereby single strains or strain mixtures are introduced into production systems through inundative/inoculative release. The approach has significant barriers that have long been recognized, including a generally limited spectrum of target pathogens for any given biocontrol agent and inadequate colonization of the host rhizosphere, which can plague progress in the utilization of this resource in commercial field-based crop production systems. Thus, although potential exists, this model has continued to lag in its application. New omics’ tools have enabled more rapid screening of microbial populations allowing for the identification of strains with multiple functional attributes that may contribute to pathogen suppression. Similarly, these technologies also enable the characterization of consortia in natural systems which provide the framework for construction of synthetic microbiomes for disease control. Harnessing the potential of the microbiome indigenous to agricultural soils for disease suppression through application of specific management strategies has long been a goal of plant pathologists. Although this tactic also possesses limitation, our enhanced understanding of functional attributes of suppressive soil systems through application of community and metagenomic analysis methods provide opportunity to devise effective resource management schemes. As these microbial communities in large part are fostered by the resources endemic to soil and the rhizosphere, substrate mediated recruitment of disease-suppressive microbiomes constitutes a practical means to foster their establishment in crop production systems.

scholarly journals Plant Disease Detection and Monitoring Using Artificial Neural Network

2022 ◽  
Vol 10 (01) ◽  
pp. 715-722
Author(s):  
Stella I. Orakwue ◽  
Nkolika O. Nwazor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Production ◽  
Plant Disease ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Major Threat ◽  
The Neural Network ◽  
Artificial Neural ◽  
Hyperspectral Sensor

Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.

scholarly journals Plant Disease Detection and Localization using GRADCAM

2020 ◽  
Vol 8 (6) ◽  
pp. 3069-3075
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Financial Resources ◽  
Disease Detection ◽  
The World ◽  
Detection And Localization ◽  
Better Than ◽  
Made In

Plant diseases are diseases that change or disrupt its important functions. The reduction in the age at which a plant dies is the main danger of plant diseases. And farmers around the world have to face the challenge of identifying and classifying these diseases and changing their treatments for each disease. This task becomes more difficult when they have to rely on naked eyes to identify diseases due to the lack of proper financial resources. But with the widespread use of smartphones by farmers and advances made in the field of deep learning, researchers around the world are trying to find a solution to this problem. Similarly, the purpose of this paper is to classify these diseases using deep learning and localize them on their respective leaves. We have considered two main models for classification called resnet and efficientnet and for localizing these diseases we have used GRADCAM and CAM. GRADCAM was able to localize diseases better than CAM

scholarly journals Leaf Disease Detection using Deep Learning Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 3172-3175
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Image Feature ◽  
Disease Detection ◽  
Proper Solution ◽  
Classification Image ◽  
Deep Learning Algorithm ◽  
Crop Diseases ◽  
Crop Disease

India is a nation of agriculture and over 70 per cent of our population relies on farming. A portion of our national revenue comes from agriculture. Agriculturalists are facing loss due to various crop diseases and it becomes tedious for cultivators to monitor the crop regularly when the cultivated area is huge. So the plant disease detection is important in agriculture field. Timely and accurate disease detection is important for the loss caused due to crop diseases which affects adversely on crop quality and yield. Early diagnosis and intervention can reduce the loss of plant due to disease and reduce the unnecessary drug usage. Earlier, automatic detection of plant disease was performed by image processing. For disease detection and classification, image processing tools and the machine learning mechanism are proposed. Crop disease will be detected through various stages of image processing such as image acquisition, pre-processing of image, image feature extraction, feature classification, disease prediction and fertilizer recommendation.detection of disease is important because it will may help farmers to provide proper solution to prevent these disease.

scholarly journals RNA interference approaches for plant disease control

BioTechniques ◽  
2020 ◽  
Vol 69 (6) ◽  
pp. 469-477
Author(s):  
Yen-Wen Kuo ◽  
Bryce W Falk
Keyword(s):  
Rna Interference ◽  
Disease Control ◽  
Crop Production ◽  
Plant Disease ◽  
Plant Pathogens ◽  
Control Plant ◽  
Cost Effective ◽  
Plant Diseases ◽  
Natural Ecosystems ◽  
Plant Disease Control

Plant diseases caused by a variety of pathogens can have severe effects on crop plants and even plants in natural ecosystems. Despite many effective conventional approaches to control plant diseases, new, efficacious, environmentally sound and cost-effective approaches are needed, particularly with our increasing human population and the effects on crop production and plant health caused by climate change. RNA interference (RNAi) is a gene regulation and antiviral response mechanism in eukaryotes; transgenic and non transgenic plant-based RNAi approaches have shown great effectiveness and potential to target specific plant pathogens and help control plant diseases, especially when no alternatives are available. Here we discuss ways in which RNAi has been used against different plant pathogens, and some new potential applications for plant disease control.

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