Image recognition of plant diseases based on backpropagation networks

Wang, Haiguang; Li, Guanlin; Ma, Zhanhong; Li, Xiaolong

doi:10.1109/cisp.2012.6469998

Cited by 85 publications

(50 citation statements)

References 9 publications

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“…Three parts in turn connect through the collection weight value between nodes [11,15]. The largest characteristic of BP network is that network weight value reach expectations through the sum of error squares between the network output and the sample output, and then it continuously adjusted network structure's weight value [8,12,13]. It is popular and extensively used for training feed forward networks.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…This has been resulted in reduced dimensional feature space. Back-propagation (BP) networks have been used to classify the grape and wheat diseases by Haiguang Wang et al [8]. Also by using principal component analysis (PCA) dimensions of the feature data has been reduced.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is sensitive to the relative scaling of the original variables [15]. PCA is the simplest of the true eigenvector-based multivariate analyses; PCA is closely related to factor analysis [8].…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Segmentation is also done through Clustering [8]. The image is converted into histogram and then clustering is done on it.…”

Section: Feature Based Clusteringmentioning

confidence: 99%

See 3 more Smart Citations

An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

Gavhale¹,

Gawande²

2014

IOSRJCE

177

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

“…Segmentation is also done through Clustering [8]. The image is converted into histogram and then clustering is done on it.…”

Section: Feature Based Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

Gavhale¹,

Gawande²

2014

IOSRJCE

177

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show abstract

“…Author defined a color segmentation approach to detect the critical image area using ROI extraction approach and later on applied the predictive classifier to perform the disease detection and classification. Haiguang Wang [6] has defined an effective recognition approach using back propagation network. Author defined a work for automatic diagnosis of plant diseases and improves the plant disease under disease type.…”

Section: Related Workmentioning

confidence: 99%

A Clustering Adaptive Neural Network Approach for Leaf Disease Identification

Chahal¹,

Anuradha²

2015

IJCA

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Agricultural image processing is having its significance to classify the agricultural objects as well as to identify the disease. These diseases are specific to the agricultural object or generic in nature. In this paper, an effective way is defined to identify the leaf disease over the plants. The work is here defined based on moment based analysis defined under clustering improved neural network approach. The work is here improved as a layered clustered and classification approach. In first stage, the identification of disease ROI is done. This identification is done using the CMeans Clustering Approach. Once the disease ROI is identified, the next work is to perform neural network approach for disease identification. The obtained results shows the effective recognition of disease area over the leaf images.

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Recent advances in agricultural disease image recognition technologies: A review

Bondre

Patil

2023

Concurrency and Computation

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In the world of intelligent agriculture, agricultural disease picture detection plays a critical role. Plant disease identification must be efficient if agricultural production is to be increased sustainably. Anomalies in plants affected by viruses, insects, nutritional deficiencies, or poor weather conditions have traditionally been diagnosed by human experts. Deep learning (DL) and transfer learning, two sophisticated machine learning techniques synonymous with the advancement of artificial intelligence technologies in recent years, have started to be used for the identification of agricultural diseases.However, a variety of significant obstacles exist in the way of widespread use of these approaches. This paper looks at DL and transfer learning in particular, and discusses recent developments in using these advanced technologies to recognize agricultural disease images. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. The analysis and evaluation of these two approaches show that transfer learning is the better choice given existing agricultural disease data tools. The paper then looks at the key problems that need to be addressed for research in this area to advance, such as image dataset creation, the collection of big data auxiliary domains, and the optimization of the transfer learning process. The construction of technically feasible agricultural disease image recognition systems requires the creation of image datasets collected under real cultivation conditions. This survey aims to learn more about DL capabilities in plant disease detection to increase device efficiency and accuracy in future research.

show abstract

Image recognition of plant diseases based on backpropagation networks

Cited by 85 publications

References 9 publications

An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

A Clustering Adaptive Neural Network Approach for Leaf Disease Identification

Recent advances in agricultural disease image recognition technologies: A review

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