Use of deep learning techniques for identification of plant leaf stresses: A review
The use of deep networks in agriculture has increased enormously in the last decade
including their use to classify different plant leaf stresses. More recently, a large number of …
including their use to classify different plant leaf stresses. More recently, a large number of …
Development of Efficient CNN model for Tomato crop disease identification
Tomato is an important vegetable crop cultivated worldwide coming next only to potato.
However, the crop can be damaged due to various diseases. It is important for the farmer to …
However, the crop can be damaged due to various diseases. It is important for the farmer to …
A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
T Lu, B Han, L Chen, F Yu, C Xue - Scientific Reports, 2021 - nature.com
A generic intelligent tomato classification system based on DenseNet-201 with transfer
learning was proposed and the augmented training sets obtained by data augmentation …
learning was proposed and the augmented training sets obtained by data augmentation …
Systematic study on deep learning-based plant disease detection or classification
Plant diseases impact extensively on agricultural production growth. It results in a price hike
on food grains and vegetables. To reduce economic loss and to predict yield loss, early …
on food grains and vegetables. To reduce economic loss and to predict yield loss, early …
Artificial Neural Network‐Based Deep Learning Model for COVID‐19 Patient Detection Using X‐Ray Chest Images
M Shorfuzzaman, M Masud… - Journal of …, 2021 - Wiley Online Library
The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID‐
19) outbreak that has affected nearly 216 countries and territories across the globe. Since …
19) outbreak that has affected nearly 216 countries and territories across the globe. Since …
PlaNet: a robust deep convolutional neural network model for plant leaves disease recognition
Researchers are looking for new ideas that can greatly increase the amount of food grown
while also cutting costs. For precision agriculture to work, pests, weeds, and diseases must …
while also cutting costs. For precision agriculture to work, pests, weeds, and diseases must …
Disease detection and identification of rice leaf based on improved detection transformer
H Yang, X Deng, H Shen, Q Lei, S Zhang, N Liu - Agriculture, 2023 - mdpi.com
In recent years, the domain of diagnosing plant afflictions has predominantly relied upon the
utilization of deep learning techniques for classifying images of diseased specimens; …
utilization of deep learning techniques for classifying images of diseased specimens; …
[HTML][HTML] A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases
In agriculture, one of the most challenging tasks is the early detection of plant diseases. It is
essential to identify diseases early in order to boost agricultural productivity. This problem …
essential to identify diseases early in order to boost agricultural productivity. This problem …
Reinforced XGBoost machine learning model for sustainable intelligent agrarian applications
D Elavarasan, DR Vincent - Journal of Intelligent & Fuzzy …, 2020 - content.iospress.com
The development in science and technical intelligence has incited to represent an extensive
amount ofdata from various fields of agriculture. Therefore an objective rises up for the …
amount ofdata from various fields of agriculture. Therefore an objective rises up for the …
Machine health management system using moving average feature with bidirectional long-short term memory
A Mubarak, M Asmelash… - Journal of …, 2023 - asmedigitalcollection.asme.org
In today's highly competitive industrial environment, machine health management systems
become a crucial factor for sustainability and success. The traditional feature extraction …
become a crucial factor for sustainability and success. The traditional feature extraction …