Plant Disease Detection based on Deep Learning Approach

2021 ◽  
Author(s):  
Faizan Akhtar ◽  
N. Partheeban ◽  
A. Daniel ◽  
Srinivasan Sriramulu ◽  
Saloni Mehra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection ◽  
Learning Approach

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 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

<i>Developing an aeroponic smart experimental greenhouse for controlling irrigation and plant disease detection using deep learning and IoT</i>

2021 ◽  
Author(s):  
Mohammadreza Narimani ◽  
Ali Hajiahmad ◽  
Ali Moghimi ◽  
Reza Alimardani ◽  
Shahin Rafiee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection
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