Artificial Intelligence in Potato Leaf Disease Classification: A Deep Learning Approach

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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Artificial Intelligence to Identify the Nectar Site: A Deep Learning Approach

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) ◽

10.1109/icaica52286.2021.9498148 ◽

2021 ◽

Author(s):

Dongpu Zhang ◽

Suling Jia ◽

Ji'An Zhao

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Learning Approach

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Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

Information ◽

10.3390/info11020095 ◽

2020 ◽

Vol 11 (2) ◽

pp. 95 ◽

Cited By ~ 1

Author(s):

Kaizhou Li ◽

Jianhui Lin ◽

Jinrong Liu ◽

Yandong Zhao

Keyword(s):

Deep Learning ◽

Ginkgo Biloba ◽

Plant Disease ◽

Disease Classification ◽

Field Conditions ◽

Laboratory Conditions ◽

Leaf Disease ◽

And Training ◽

Ginkgo Leaf

Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.

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An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

Alinteri Journal of Agricultural Sciences ◽

10.47059/alinteri/v36i1/ajas21065 ◽

2021 ◽

Vol 36 (1) ◽

pp. 443-450

Author(s):

Mounika Jammula

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Plant Disease ◽

Bilateral Filter ◽

Noise Removal ◽

Classification Performance ◽

Disease Classification ◽

Gaussian Kernel ◽

Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

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A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

10.26434/chemrxiv.14453106.v1 ◽

2021 ◽

Author(s):

Oscar Méndez-Lucio ◽

Mazen Ahmad ◽

Ehecatl Antonio del Rio-Chanona ◽

Jörg Kurt Wegner

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Molecular Target ◽

Bioactive Molecules ◽

Target Pair ◽

Learning Approach ◽

Scoring Functions ◽

Structure Based Drug Design ◽

Protein Targets ◽

Better Than

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

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