Plant Disease Detection Using Deep Learning (Convolutional Neural Networks)

2021 ◽  
pp. 635-649
Author(s):  
Nidhi Prashar ◽  
A. L. Sangal
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Disease Detection

Convolutional Neural Networks-based plant disease detection implemented on low-power consumption device

2021 ◽  
Author(s):  
Eduardo A. Huerta Mora ◽  
Victor Gonzalez-Huitron ◽  
A.E. Rodriguez-Mata ◽  
Hector Rodriguez Rangel
Keyword(s):  
Neural Networks ◽  
Power Consumption ◽  
Low Power ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Low Power Consumption ◽  
Disease Detection

scholarly journals Pneumonia and Eye Disease Detection using Convolutional Neural Networks

2020 ◽  
Vol 10 (3) ◽  
pp. 5769-5774 ◽  
Author(s):  
P. Chakraborty ◽  
C. Tharini
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Eye Disease ◽  
Medical Professionals ◽  
Disease Detection ◽  
Training Time ◽  
X Ray ◽  
Detection Systems

Automatic disease detection systems based on Convolutional Neural Networks (CNNs) are proposed in this paper for helping the medical professionals in the detection of diseases from scan and X-ray images. CNN based classification helps decision making in a prompt manner with high precision. CNNs are a subset of deep learning which is a branch of Artificial Intelligence. The main advantage of CNNs compared to other deep learning algorithms is that they require minimal pre-processing. In the proposed disease detection system, two medical image datasets consisting of Optical Coherence Tomography (OCT) and chest X-ray images of 1-5 year-old children are considered and used as inputs. The medical images are processed and classified using CNN and various performance measuring parameters such as accuracy, loss, and training time are measured. The system is then implemented in hardware, where the testing is done using the trained models. The result shows that the validation accuracy obtained in the case of the eye dataset is around 90% whereas in the case of lung dataset it is around 63%. The proposed system aims to help medical professionals to provide a diagnosis with better accuracy thus helping in reducing infant mortality due to pneumonia and allowing finding the severity of eye disease at an earlier stage.

scholarly journals How Convolutional Neural Networks Diagnose Plant Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yosuke Toda ◽  
Fumio Okura
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Disease Diagnosis ◽  
Black Box ◽  
Plant Diseases ◽  
Training Dataset ◽  
Great Success ◽  
Human Decision

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

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.

Plant disease detection with convolutional neural networks implemented on Raspberry Pi 4

2020 ◽  
Author(s):  
Eduardo Alfonso Huerta Mora ◽  
Victor Alejandro Gonzalez Huitron ◽  
Abraham Efraim Rodriguez Mata ◽  
Hector Rodriguez Rangel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Raspberry Pi ◽  
Disease Detection
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