Fruit and Leaves Disease Prediction Using Deep Learning Algorithm

2019 ◽  
pp. 8-16
Author(s):  
Sathya V ◽  
Rafidha H ◽  
Sumitha Rani G
Keyword(s):  
Plant Disease ◽  
Learning Algorithm ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Production Plant ◽  
Deep Learning Algorithm ◽  
Long Time ◽  
The Cost ◽  
And Control

The purpose of Agriculture is not only to feed ever growing population but it’s an important source of energy and a solution to solve the problem of global warming. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Generally the naked eye method is used to identify the diseases. In this method experts are involved who have the ability to detect the changes in leaf color. This method involves lots of efforts, takes long time and also not practical for the large fields. Many times different experts identify the same disease as the different disease. This method is expensive as it requires continuous monitoring of experts. Tree leaves and fruit diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. The producers need to monitor their crops and detect the first symptoms in order to prevent the spread of a plant disease, with low cost and save the major part of the production. Hiring professional agriculturists may not be affordable especially in remote isolated geographic regions. Machine learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes image segmentation and image classification approach to predict various types of diseases using Otsu thresholding method and convolutional neural network method.

scholarly journals Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes

2021 ◽  
Vol 12 ◽  
Author(s):  
Alvaro Fuentes ◽  
Sook Yoon ◽  
Mun Haeng Lee ◽  
Dong Sun Park
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Plant Disease ◽  
High Efficiency ◽  
Recognition Rate ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Improve Model ◽  
Improving Accuracy ◽  
And Control

Recognizing plant diseases is a major challenge in agriculture, and recent works based on deep learning have shown high efficiency in addressing problems directly related to this area. Nonetheless, weak performance has been observed when a model trained on a particular dataset is evaluated in new greenhouse environments. Therefore, in this work, we take a step towards these issues and present a strategy to improve model accuracy by applying techniques that can help refine the model’s generalization capability to deal with complex changes in new greenhouse environments. We propose a paradigm called “control to target classes.” The core of our approach is to train and validate a deep learning-based detector using target and control classes on images collected in various greenhouses. Then, we apply the generated features for testing the inference of the system on data from new greenhouse conditions where the goal is to detect target classes exclusively. Therefore, by having explicit control over inter- and intra-class variations, our model can distinguish data variations that make the system more robust when applied to new scenarios. Experiments demonstrate the effectiveness and efficiency of the proposed approach on our extended tomato plant diseases dataset with 14 classes, from which 5 are target classes and the rest are control classes. Our detector achieves a recognition rate of target classes of 93.37% mean average precision on the inference dataset. Finally, we believe that our study offers valuable guidelines for researchers working in plant disease recognition with complex input data.

Chilli Plant Disease Detection and Classification using DenseNet CNN Approach

2021 ◽  
Vol 9 (VI) ◽  
pp. 4469-4477
Author(s):  
Sheenu N V
Keyword(s):  
Precision Agriculture ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Plant Diseases ◽  
Disease Detection ◽  
Deep Learning Algorithm ◽  
Affected Plant ◽  
Main Motive ◽  
Important People ◽  
Micro Organisms

To fulfil the food requirement and economic growth, farming plays a very important role. Thus Farmers are the most important people in the world. Be it the smallest or the largest country, Because of them only we are able to live on the planet. Precision agriculture is the new trending term in the field of technology whose main motive is to reduce the workload of the farmers and increase the productivity of the farms by using technologies. So the aim of this work is to detect the disease of the plant by classifying their leaves using deep learning algorithm. For this work chilli plants are considered, because of their economic importance. And there are various problems in chilli production due to the presence of various micro-organisms and pathogens and The plant disease detection can be done by observing the spot on the leaves of the affected plant. The method here adopting to detect plant diseases is image processing using Dense Net based Convolution neural network (CNN). CNN will be used for leaf image classification and will produce the good results with a good accuracy.

scholarly journals An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7159-7164 ◽  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Disease Detection ◽  
Crop Species ◽  
Learning Techniques ◽  
Proposed Model ◽  
Significant Performance

Plant disease detection is used to detect and identify symptoms of plant diseases. Detection of plant diseases through the naked eye is ineffective, especially because there are numerous diseases. Therefore, there is a need to develop low-cost methods to improve rapidity and accuracy of plant disease diagnosis. This paper presents an effective model for plant disease detection by using our developed deep learning approach. Extensive experiments were performed on the PlantVillage dataset, which contains 54,306 images categorized between 38 different classes containing 14 crop species and 26 diseases. Our proposed model demonstrated significant performance improvement in terms of accuracy, recall, precision, and F1-score compared with the existing model used for plant disease detection.

scholarly journals Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3515
Author(s):  
Sung-Ho Sim ◽  
Yoon-Su Jeong
Keyword(s):  
Low Cost ◽  
Third Party ◽  
Monitoring And Control ◽  
Constant Size ◽  
Computing Environments ◽  
Iot Devices ◽  
Cloud Servers ◽  
The Cost ◽  
Processing Techniques ◽  
And Control

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

scholarly journals Research on Efficient Deep Learning Algorithm Based on ShuffleGhost in the Field of Virtual Reality

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bangtong Huang ◽  
Hongquan Zhang ◽  
Zihong Chen ◽  
Lingling Li ◽  
Lihua Shi
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Maps ◽  
Embedded Devices ◽  
Feature Map ◽  
Deep Learning Algorithm ◽  
Proper Design ◽  
The Cost

Deep learning algorithms are facing the limitation in virtual reality application due to the cost of memory, computation, and real-time computation problem. Models with rigorous performance might suffer from enormous parameters and large-scale structure, and it would be hard to replant them onto embedded devices. In this paper, with the inspiration of GhostNet, we proposed an efficient structure ShuffleGhost to make use of the redundancy in feature maps to alleviate the cost of computations, as well as tackling some drawbacks of GhostNet. Since GhostNet suffers from high computation of convolution in Ghost module and shortcut, the restriction of downsampling would make it more difficult to apply Ghost module and Ghost bottleneck to other backbone. This paper proposes three new kinds of ShuffleGhost structure to tackle the drawbacks of GhostNet. The ShuffleGhost module and ShuffleGhost bottlenecks are utilized by the shuffle layer and group convolution from ShuffleNet, and they are designed to redistribute the feature maps concatenated from Ghost Feature Map and Primary Feature Map. Besides, they eliminate the gap of them and extract the features. Then, SENet layer is adopted to reduce the computation cost of group convolution, as well as evaluating the importance of the feature maps which concatenated from Ghost Feature Maps and Primary Feature Maps and giving proper weights for the feature maps. This paper conducted some experiments and proved that the ShuffleGhostV3 has smaller trainable parameters and FLOPs with the ensurance of accuracy. And with proper design, it could be more efficient in both GPU and CPU side.

scholarly journals Biochar and Trichoderma spp. in management of plant diseases caused by soilborne fungal pathogens: a review and perspective

2021 ◽  
Vol 10 (15) ◽  
pp. e296101522465
Author(s):  
Erika Valente de Medeiros ◽  
Lucas Figueira da Silva ◽  
Jenifer Sthephanie Araújo da Silva ◽  
Diogo Paes da Costa ◽  
Carlos Alberto Fragoso de Souza ◽  
...  
Keyword(s):  
Soil Properties ◽  
Fungal Pathogens ◽  
Plant Disease ◽  
Plant Pathogens ◽  
Plant Diseases ◽  
Management Tool ◽  
Production Plant ◽  
Trichoderma Spp ◽  
Health Concept ◽  
Plant Disease Management

A better understanding of the use of biochar with Trichoderma spp. (TRI), considered the most studied tool for biological control, would increase our ability to set priorities. However, no studies exist using the two inputs on plant disease management. Here, we hypothesized that biochar and TRI would be used for the management of soilborne plant pathogens, mainly due to changes in soil properties and its interactions. To test this hypothesis, this review assesses papers that used biochar and TRI against plant diseases and we summarize the handling mechanisms for each input. Biochar acts by mechanisms: induction to plant resistance, sorption of allelopathic and fungitoxic compounds, increase of beneficial microorganisms, changes the soil properties that promote health and nutrient availability. Trichoderma as biocontrol agents by different mechanisms: mycoparasitism, enzyme and secondary metabolic production, plant promoter agent, natural decomposition agent, and biological agent of bioremediation. Overall, our findings expand our knowledge about the reuse of wastes transformed in biochar combined with Trichoderma has potential perspective to formulate products as alternative management tool of plant disease caused by soilborne fungal pathogen and add important information that can be suitable for development of strategy for use in the global health concept.

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

scholarly journals Ribosome Display Technology: Applications in Disease Diagnosis and Control

Antibodies ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 28
Author(s):  
Adinarayana Kunamneni ◽  
Christian Ogaugwu ◽  
Steven Bradfute ◽  
Ravi Durvasula
Keyword(s):  
In Vitro Selection ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Single Chain ◽  
Single Chain Antibody ◽  
Ribosome Display ◽  
Technology Applications ◽  
Display Technology ◽  
And Control

Antibody ribosome display remains one of the most successful in vitro selection technologies for antibodies fifteen years after it was developed. The unique possibility of direct generation of whole proteins, particularly single-chain antibody fragments (scFvs), has facilitated the establishment of this technology as one of the foremost antibody production methods. Ribosome display has become a vital tool for efficient and low-cost production of antibodies for diagnostics due to its advantageous ability to screen large libraries and generate binders of high affinity. The remarkable flexibility of this method enables its applicability to various platforms. This review focuses on the applications of ribosome display technology in biomedical and agricultural fields in the generation of recombinant scFvs for disease diagnostics and control.

scholarly journals An overview of activation functions used in Neuronal Networks: Application for plant disease diagnosis

2021 ◽  
Vol 10 (1) ◽  
pp. 139-145
Keyword(s):  
Plant Disease ◽  
Transfer Functions ◽  
Activation Function ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Automatic Identification ◽  
Relevant Parameter ◽  
Activation Functions ◽  
Crop Fields ◽  
Learning Techniques

Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Many applications in automatic identification of plant diseases have been developed. This work adopts a new approach that focuses on studying a relevant parameter that make a significant impact on the performance of CNNs, namely, the variants of activation function, particularly the most famous used functions and their influence on the model’s performance and accuracy. We will also present the different types of activation functions, which are also called transfer functions. Then, and through a case study application to the plant disease detection, we will have the opportunity to compare the results of these different functions with a graphical presentation using evaluation metrics, such as accuracy functions and loss functions as Binary Cross-Entropy. The training of the models was carried out using a free accessible database of 20,639 photographs, taken both in the laboratory and in real conditions from the crop fields. The data includes three plant species in fifteen distinct classes of combinations [plant, disease], including some healthy plants.

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