Mango Leaf Stress Identification Using Deep Neural Network

被引:5
作者
Gautam, Vinay [1 ]
Rani, Jyoti [2 ]
机构
[1] DIT Univ, Dehra Dun 248003, Uttarakhand, India
[2] Chandigah Univ, Mohali 175001, India
关键词
Convolutional neural network (CNN); artificial intelligence (AI); biotic disease; segmentation; image processing; ABIOTIC STRESS; CLASSIFICATION; LEAVES;
D O I
10.32604/iasc.2022.025113
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mango is a widely growing and consumable fruit crop. The quantity and quality of production are most important to satisfy the needs of the huge population. Numerous research has been conducted to increase the yield of the crop. But a good number of crop harvests were destroyed due to various factors and leaf stress is one of them. The various types of stresses include biotic and abiotic that impact the mangoes productivity. But here the focus is on biotic stress factors such as fungus and bacteria. The effect of the stress can be reduced in the preliminary stage by taking some affirmative steps such as earlier detection and resolutions for the same. So many advanced techniques and methods have been used extensively to identify and classify certain stresses. This research supports farmers in identifying early-stage stresses in the plant leaf, that enhances the mango quality and quantity. This approach???s main objective is to ensure that farmers are accurately informed and driven by accurate results of the diagnosis based on affirmative knowledge. The complete approach is laid down in two folds. Firstly, the region of interest is segmented from input images. The region of interest is used to extract symmetrical features of images and these features are used to create symmetry in the classification and identification. Secondly, the segmented image is processed through Convolutional Neural Network (CNN). CNN model is a multilayer model that automatically extracts features from the inputted image. The proposed technique is compared with various models such as VGG16, VGG19, and RestNet, etc. The proposed technique in this paper outperforms other models. The whole experiment was performed using Google Co-Lab using dataset from a known standard or open-source repository. The data collection is composed of different diseases characteristics compared to a healthy leaf. The different stress approach was chosen for different types of mango leaves like powdery mildew, anthracnose, dieback, phoma blight, bacterial canker, and red rust. to identify stress and can be beneficial in actual application by farmers.
引用
收藏
页码:849 / 864
页数:16
相关论文
共 60 条
[1]   Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique [J].
Abdulridha, Jaafar ;
Ehsani, Reza ;
de Castro, Ana .
AGRICULTURE-BASEL, 2016, 6 (04)
[2]  
Al Bashish Dheeb, 2011, Information Technology Journal, V10, P267, DOI 10.3923/itj.2011.267.275
[3]  
Arivazhagan S., 2013, Agricultural Engineering International: CIGR Journal, V15, P211
[4]  
Arivazhagan S., 2018, Int. J. Pure Appl. Mathematics, V120, P11067
[5]  
Barenboim G., 2021, ARXIV PREPRINT ARXIV
[6]   Smart Farming: Pomegranate Disease Detection Using Image Processing [J].
Bhange, Manisha ;
Hingoliwala, H. A. .
SECOND INTERNATIONAL SYMPOSIUM ON COMPUTER VISION AND THE INTERNET (VISIONNET'15), 2015, 58 :280-288
[7]  
Chouhan Siddharth Singh, 2019, 2019 4th International Conference on Information Systems and Computer Networks (ISCON), P700, DOI 10.1109/ISCON47742.2019.9036158
[8]   Impact of Chemical Oxidation on Brazilian Soils [J].
da Silva, Leandro A. ;
Teixeira, Silvio C. G. ;
Perez, Daniel V. ;
Marques, Monica R. da C. .
JOURNAL OF THE BRAZILIAN CHEMICAL SOCIETY, 2012, 23 (02) :367-371
[9]   Prediction of Occupation Stress by Implementing Convolutional Neural Network Techniques [J].
Dalal, Surjeet ;
Khalaf, Osamah Ibrahim .
JOURNAL OF CASES ON INFORMATION TECHNOLOGY, 2021, 23 (03) :27-42
[10]   Identification of malnutrition and prediction of BMI from facial images using real-time image processing and machine learning [J].
Dhanamjayulu, C. ;
Nizhal, U. N. ;
Maddikunta, Praveen Kumar Reddy ;
Gadekallu, Thippa Reddy ;
Iwendi, Celestine ;
Wei, Chuliang ;
Xin, Qin .
IET IMAGE PROCESSING, 2022, 16 (03) :647-658