Journal ArticleDOI
Prediction of diseased rice plant using video processing and LSTM-simple recurrent neural network with comparative study
Toran Verma,Sipi Dubey +1 more
TLDR
In this article, a new model using mobile video image processing and Long Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability.Abstract:
The disease infliction of the plants severely influences the yield. It alters the essence and extent of crop production cause fiscal distress. Consequently, the diagnosis of numerous plant diseases is significant to decrease the yield perdition by discovering crop infections at their earlier stages. This paper introduces a new model using mobile video image processing and Long-Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability. The rice plant videos captured under uncontrolled conditions in day-lighting using a mobile handset and divided into two sections for the designing and testing of LSTM-SRNN models. After shooting, the video images of the rice plant segmented using colour indexing and linear color space transformation with minimal daylight impact. Low-level spatial features; entropy, standard deviation, and fuzzy features extracted after video image segmentation. The excerpted characteristics with the composite combinations transformed in time-series datasets with the desired response. The datasets employed in the LSTM-SRNN model for progressive learning. The distinct test video features applied in LSTM-SRNN to appraise the generalization capability of the proposed system with performance analysis. The experimental outcomes of the proposed LSTM-SRNN model exhibit 99.99% prediction ability with fuzzy features. The model also presents possibilities for dynamic learning adaptability and temporal information processing to overcome the limitations of many well-known rule-based and machine learning approaches.read more
Citations
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Journal ArticleDOI
A novel approach for rice plant diseases classification with deep convolutional neural network
TL;DR: In this paper, the authors proposed an effective approach for recognition and identification of rice plant disease based on size, shape and color of lesions in the leaf image, which achieved an accuracy of 99.7% on the dataset.
Proceedings ArticleDOI
Rice Leaves Disease Detection and Classification Using Transfer Learning Technique
Rukhsar,Santosh Kumar Upadhyay +1 more
TL;DR: In this article , the authors used transfer learning techniques to identify rice leaf diseases such as blast, blight, and tungro, which can be detected early using transfer learning models, allowing to prevent disease spread over the entire plant and thus increasing the rice yields.
Journal ArticleDOI
Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification
TL;DR: In this article , the authors proposed a novel network where it combines dilated convolution with residual dense block (DCRDB) along with multi-level feature detection (MLFD) for selecting the appropriate feature and bidirectional long short-term memory (Bi-LSTM) classifier for leaf disease prediction.
Journal ArticleDOI
A computer aided plant leaf classification based on optimal feature selection and enhanced recurrent neural network
Bhanuprakash Dudi,V. Rajesh +1 more
TL;DR: In this paper , the authors developed a new plant leaf classification model based on enhanced segmentation and optimal feature selection, in which the first process is the pre-processing, and RGB to grey-scale conversion, histogram equalisation, and median filtering are adopted.
Journal ArticleDOI
Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video
TL;DR: The purpose is to build a better intelligent transport platform and improve the performance of surveillance video abnormal behavior detection systems under rapid progress of science and technology, to process large-scale traffic surveillance video data.
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