Deep Learning-Based Knowledge Extraction From Diseased and Healthy Edible Plant Leaves

2021 ◽  
Vol 12 (2) ◽  
pp. 67-81
Author(s):  
Udit Jindal ◽  
Sheifali Gupta
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection ◽  
Edible Plant ◽  
Research Article ◽  
Segmented Images ◽  
Significant Area ◽  
Testing Accuracy ◽  
Disease Present

Agriculture contributes majorly to all nations' economies, but crop diseases are now becoming a very big issue that has to be resolving immediately. Because of this, crop/plant disease detection becomes a very significant area to work. However, a huge number of studies have been done for automatic disease detection using machine learning, but less work has been done using deep learning with efficient results. The research article presents a convolution neural network for plant disease detection by using open access ‘PlantVillage' dataset for three versions that are colored, grayscale, and segmented images. The dataset consists of 54,305 images and is being used to train a model that will be able to detect disease present in edible plants. The proposed neural network achieved the testing accuracy of 99.27%, 98.04%, and 99.14% for colored, grayscale, and segmented images, respectively. The work also presents better precision and recall rates on colored image datasets.

scholarly journals Plant Disease Detection using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 909-914
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection ◽  
Virus Identification ◽  
Detection Model ◽  
Agriculture Industry ◽  
Whole Plant ◽  
Proposed Model ◽  
Testing Accuracy

[Context] Plants play an essential role in climate change, agriculture industry and a country’s economy. Thereby taking care of plants is very crucial. Just like humans, plants are effected by several disease caused by bacteria, fungi and virus. Identification of these disease timely and curing them is essential to prevent whole plant from destruction. [Objective] This paper proposes a deep learning based model named plant disease detector. The model is able to detect several diseases from plants using pictures of their leaves. [Methodology] Plant disease detection model is developed using neural network. First of all augmentation is applied on dataset to increase the sample size. Later Convolution Neural Network (CNN) is used with multiple convolution and pooling layers. PlantVillage dataset is used to train the model. After training the model, it is tested properly to validate the results. [Results] We have performed different experiments using this model. 15% of data from PlantVillage data is used for testing purpose that contains images of healthy as well as diseased plants. Proposed model has achieved 98.3% testing accuracy. [Conclusion] This study is focused on deep learning model to detect disease in plant leave. But, in future model can be integrated with drone or any other system to live detect diseases from plants and report the diseased plants location to people so that they can be cured accordingly.

An Application of Machine Learning in Plant Leaves Disease Detection and Classification

2020 ◽  
Vol 17 (6) ◽  
pp. 2645-2652
Author(s):  
Sachin Dahiya ◽  
Tarun Gulati
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Automatic System ◽  
Crop Production ◽  
Plant Disease ◽  
Data Augmentation ◽  
Machine Intelligence ◽  
Disease Detection ◽  
Initial Stage

Plant disease severely affects the crop production. Food security is always a challenge because the population of the world is increasing at a rapid rate. Diseases in plants can be controlled at the initial stage with the help of automatic system that can be able to detect the wide variety of diseases before its spreading to the whole cultivation area. With the development of various machine learning and deep learning algorithms it is now possible to design such an automatic system. Deep neural network like convolution neural network are able to detect the plant disease with high accuracy. In this paper we have discussed about the deep learning techniques, CNN and its parameters, data augmentation, transfer learning and various factor that affects the performance of DL model. Recent studies that apply the machine intelligence in plant leaf disease detection are also discussed.

Challenges and Issues in Plant Disease Detection Using Deep Learning

2021 ◽  
pp. 56-74
Author(s):  
Priyanka Sahu ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Dinesh Singh ◽  
Ravinder Pal Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Plant Disease ◽  
Image Database ◽  
Plant Diseases ◽  
Disease Detection ◽  
Factors Influencing ◽  
Classification Tasks ◽  
Main Factors

Deep learning (DL) has rapidly become an essential tool for image classification tasks. This technique is now being deployed to the tasks of classifying and detecting plant diseases. The encouraging results achieved with this methodology hide many problems that are rarely addressed in related experiments. This study examines the main factors influencing the efficiency of deep neural networks for plant disease detection. The challenges discussed in the study are based on the literature as well as experiments conducted using an image database, which contains approximately 1,296 leaf images of the beans crop. A pre-trained convolutional neural network, EfficientNet B0, is used for training and testing purposes. This study gives and emphasizes on factors and challenges that may potentially affect the use of DL techniques to detect and classify plant diseases. Some solutions are also suggested that may overcome these problems.

scholarly journals Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

2021 ◽  
Author(s):  
Hepzibah Elizabeth David ◽  
K. Ramalakshmi ◽  
R. Venkatesan ◽  
G. Hemalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Computational Effort ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Tomato Production ◽  
Classification Image ◽  
Leaf Disease ◽  
Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

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.

scholarly journals Plant Disease Detection Using Deep Learning

2021 ◽  
Vol 3 (Special Issue ICARD 3S) ◽  
pp. 30-33
Author(s):  
Kowshik B ◽  
Savitha V ◽  
Nimosh madhav M ◽  
Karpagam G ◽  
Sangeetha K
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection
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