Infection Segmentation of Leaves Using Deep Learning techniques to enhance crop productivity in smart agriculture

Karennagari, Nikhitha; Reddy, K Yashwanth; Gurrala, Vijaya Kumar; Srinivas, K. Kalyana; Peddi, Anudeep; Sai, Y. Padma

doi:10.1109/ispcc53510.2021.9609379

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The most effective and successful approach is one that integrates each of these techniques [15][16][17] [18].…”

Section: A Paddy Crop Diseases and Their Impactmentioning

confidence: 99%

Impact and Analysis of Disease Spread in Paddy Crops using Environmental Factors with the Support of X-Step Algorithm

Prakash¹,

Srivenkatesh²

2023

IJACSA

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India is an agriculture-based country, with paddy being the main crop cultivated on nearly half of its agricultural lands. Paddy cultivation faces numerous challenges, particularly diseases that affect crop growth and yield. Adult paddy crops are especially vulnerable to diseases caused by various factors, such as the green rice leafhopper, rice leaf folder, and brown plant leafhopper. These insects inflict damage on the paddy crops, restricting their growth and leading to significant losses. This research paper investigates the impact of environmental factors on disease spread in paddy crops, using the X-Step Algorithm for analysis. The study aims to better understand the role of environmental conditions, including air, water, and soil quality, in the development and progression of diseases in rice crops. This knowledge will help to optimize disease prevention and management strategies for improved crop yields and food security. The X-Step Algorithm, a novel machine learning algorithm, was employed to model and predict disease spread, taking into account various environmental factors. The proposed algorithm analyses images of paddy crops either manually captured or taken by sensors to evaluate disease spread and growth in paddy crops. This data-driven approach allows for more accurate and timely predictions, enabling farmers and agricultural experts to implement appropriate interventions.

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“…The most effective and successful approach is one that integrates each of these techniques [15][16][17] [18].…”

Section: A Paddy Crop Diseases and Their Impactmentioning

confidence: 99%

Impact and Analysis of Disease Spread in Paddy Crops using Environmental Factors with the Support of X-Step Algorithm

Prakash¹,

Srivenkatesh²

2023

IJACSA

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“…There are also many segmentation methods based level set used in agriculture, such as [8][9][10], which have achieved good results. In recent years, due to its powerful feature extraction ability, deep learning has also been widely applied in agriculture, such as [11][12][13][14][15], and achieved good results in segmentation accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Xanthoceras sorbifolium crack segmentation method based on an improved level set

Zhang

Chen

et al. 2023

IET Image Processing

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The dehiscence of the Xanthoceras sorbifolium (X. sorbifolium) may lead to seeds jump out and economic loss. The shape and the degree of the crack will provide the relevant elements for the study of the X. sorbifolium dehiscence and duly picked. Therefore, an improved level set method is proposed for X. sorbifolium crack segmentation. The problems of intensity inhomogeneity and so on, which pose challenges for accurate crack segmentation. The local Gaussian distribution fitting method has a good segmentation effect, but it is sensitive to the initial contour and does not use gradient information, which affects the accurate location of the edge. Aiming at the above problems and the scene of crack segmentation, this paper firstly adopts histogram threshold method to obtain the initial contour automatically. Secondly, the energy function is constructed by combining local and edge information. Finally, the double‐well potential function is used to reduce the oscillation and distortion of the method. In this paper, the experiment results show that the average boundary precision is 88.25% and average segmentation time of each image is 12.7s of the proposed method. After comprehensive analysis of IoU and boundary recall, the method in this paper achieves better results.

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Artificial Intelligence based Optimal Biometric Security System Using Palm Veins

Srinivas¹,

Vijitha²,

Chandra³

et al. 2022

2022 International Mobile and Embedded Technology Conference (MECON)

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Infection Segmentation of Leaves Using Deep Learning techniques to enhance crop productivity in smart agriculture

Cited by 3 publications

References 7 publications

Impact and Analysis of Disease Spread in Paddy Crops using Environmental Factors with the Support of X-Step Algorithm

Impact and Analysis of Disease Spread in Paddy Crops using Environmental Factors with the Support of X-Step Algorithm

A Xanthoceras sorbifolium crack segmentation method based on an improved level set

Artificial Intelligence based Optimal Biometric Security System Using Palm Veins

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