Estimating the Crop Acreage of Menthol Mint Crop from Remote Sensing Satellite Imagery Using ANN

被引:2
作者
Babu, Jampani Satish [1 ]
Ch, Smitha Chowdary [1 ]
Bhattacharyya, Debnath [1 ]
Byun, Yungcheol [2 ]
机构
[1] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vaddeswaram, Guntur 522302, India
[2] Jeju Natl Univ, Dept Comp Engn, 102 Jejudaehak Ro, Jeju Si 690756, Jeju Do, South Korea
来源
AGRONOMY-BASEL | 2023年 / 13卷 / 04期
关键词
precision data; smart agriculture; rainfall; crop acreage estimation; remote sensing; TIBETAN PLATEAU;
D O I
10.3390/agronomy13040951
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Acreage estimates are crucial for forecasting menthol mint acreage, as crop output figures fluctuate from year to year in response to fluctuations in the market price of menthol mint oil. Thus, there are yearly fluctuations in the maximum price that farmers can obtain. Since low production arises from low rates, and high production results from high prices, these acreage estimate studies may be useful in lowering uncertainty regarding menthol mints' output. The widespread adoption of remote sensing technologies for assessing crop acreage at both the national and international levels can be attributed to their low cost, ease of use, and flexibility. The extent of an area planted with menthol mint in the Vishakhapatnam district of Andhra Pradesh, India, was estimated using Sentinel-2A satellite data for that year. After conducting a comprehensive ground survey, the area of the menthol mint crop was estimated using an adaptive maximum chance-type set of rules for taluk-level statistics. According to the research, the Bheemunipatnam taluk in the Vishakhapatnam district was the most productive in growing menthol mint. Using customer and manufacturer accuracies of 89.13% and 87.23%, along with the average accuracy (90.67%) and kappa rate (0.9), the total acreage of menthol mint crop in the study region was estimated to be around 58,000,284.70 ha (0.844). A further aim in this study was to estimate the acreage planted with early and late menthol mint. Around 26,123.50 ha and 29,911.40 ha were found to be home to early menthol mint and late menthol mint, respectively. This method shows promise for early- and late-stage crop acreage assessment of menthol mint using a localised degree of precision.
引用
收藏
页数:18
相关论文
共 50 条
[41]   Remote sensing and US crop insurance program integrity: data mining satellite and agricultural data [J].
Little, B. ;
Schucking, M. ;
Gartrell, B. ;
Chen, B. ;
Olson, S. ;
Ross, K. ;
Jenkerson, C. ;
KcKellip, R. .
DATA MINING VIII: DATA, TEXT AND WEB MINING AND THEIR BUSINESS APPLICATIONS, 2007, 38 :151-+
[42]   Multimodal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery [J].
Gadiraju, Krishna Karthik ;
Ramachandra, Bharathkumar ;
Chen, Zexi ;
Vatsavai, Ranga Raju .
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2020, :3234-3242
[43]   Wheat Crop Field and Yield Prediction using Remote Sensing and Machine Learning [J].
Ayub, Maheen ;
Khan, Najeed Ahmed ;
Haider, Rana Zeeshan .
PROCEEDINGS OF 2ND IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (ICAI 2022), 2022, :158-164
[44]   Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques [J].
Awad, Mohamad M. .
AGRICULTURE-BASEL, 2019, 9 (03)
[45]   Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article) [J].
Ali, Abdelraouf M. ;
Abouelghar, Mohamed ;
Belal, A. A. ;
Saleh, Nasser ;
Yones, Mona ;
Selim, Adel I. ;
Amin, Mohamed E. S. ;
Elwesemy, Amany ;
Kucher, Dmitry E. ;
Maginan, Schubert ;
Savin, Igor .
EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES, 2022, 25 (03) :711-716
[46]   Forecasting land use changes in crop classification and drought using remote sensing [J].
Mashael Maashi ;
Nada Alzaben ;
Noha Negm ;
Venkatesan Veeramani ;
Sabarunisha Sheik Begum ;
Geetha Palaniappan .
Journal of Arid Land, 2025, 17 (5) :575-589
[47]   Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS [J].
Ray, SS ;
Dadhwal, VK .
AGRICULTURAL WATER MANAGEMENT, 2001, 49 (03) :239-249
[48]   Review on Crop Type Fine Identification and Automatic Mapping Using Remote Sensing [J].
Liu Z. ;
Liu D. ;
Zhu D. ;
Zhang L. ;
Zan X. ;
Tong L. .
Zhu, Dehai (zhudehai@263.net), 2018, Chinese Society of Agricultural Machinery (49) :1-12
[49]   Near Real Time Crop Loss Estimation using Remote Sensing Observations [J].
Sawant, Suryakant ;
Mohite, Jayantrao ;
Sakkan, Mariappan ;
Pappula, Srinivasu .
2019 8TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS), 2019,
[50]   DETECTION OF COVER CROP USING TIME-SERIES REMOTE SENSING OBSERVATIONS [J].
Mohite, Jayantrao ;
Sawant, Suryakant ;
Agrawal, Rishabh ;
Pandit, Ankur ;
Pappula, Srinivasu .
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, :3430-3433