Remote sensing uses electromagnetic radiation to detect and classify objects on Earth via satellite or aircraft sensors. The document discusses: - Principles of remote sensing including how different surfaces reflect different wavelengths of energy. - Applications in horticulture like monitoring crop area, disease detection, yield estimation, and disaster management. - Four case studies applying remote sensing to issues like monitoring banana disease in Brazil, estimating mango production in India, assessing land suitability for crops in Ethiopia, and evaluating suitable areas for litchi orchards in India. Challenges to adopting remote sensing in Indian horticulture include small land holdings, lack of demonstration of costs and benefits, and gaps in knowledge and expertise. Future prospects

1. Speaker: Kuldeep
M.Sc. (Ag.) Horticulture
Department of Horticulture (Fruit and Fruit Technology)
Bihar Agricultural University
Sabour, Bhagalpur-813 210, Bihar 1

2.  Introduction
 Principles of Remote sensing
 Classification of remote sensing
 Stage in Remote sensing
 Element of Remote sensing
 Application of Remote sensing
 Research Findings
 Challenges of Remote sensing for Horticulture crops
 Conclusion
Outline
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3. What is Remote Sensing ?
What is Electro-magnetic radiation ?
What is remote sensor ?
What is platform ?
Introduction
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4.  Detection and discrimination of objects or surface features means
detecting and recording radiant energy reflected or emitted by
objects or surface.
 Different objects return different amount of energy in different
bands of the electromagnetic spectrum, incident upon it.
 Depend upon the property of material (physical, structural and
chemical), surface roughness, angle of incidence, intensity and
wavelength of radiant energy.
Principles of remote sensing
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5. Moran et al., 1991
Classification of remote sensing
Based on platform:
 Ground borne
 Air borne
 Space-borne
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6. • Based on energy source
Active remote sensing
Passive remote sensing
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7. Based on region of electromagnetic spectrum
• Optical remote sensing
• Thermal remote sensing
• Microwave remote sensing
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8. Stages in remote sensing
1-Energy source or illumination(A)
2-Radiation and Atmosphere(B)
3-Intreaction with target(C)
4-Recording of energy by the sensor(D)
5-Transmition,Reception and Processing(E)
6-Interpretation and Analysis(F)
7-Application(G)
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9. ELEMENTS OF REMOTE SENSING
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10. Remote sensing process as applied to disease monitoring processes.
The sun (A) Electromagnetic energy (B) To plants (C). A portion of the
electromagnetic energy is transmitted through the leaves. The sensor on the
satellite detects the reflected energy (D). The data is then transmitted to the
ground station (E). The data is analyzed (F) And displayed on field maps.
The remote sensing process
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11.  Assessment of crop area
 Determination of Canopy measurement
Determination of fruit yield.
Quantification and scheduling of precise and optimum application of
fertilizer, irrigation and pesticides.
Identification of diseases and pest attacks.
Weather study for site specific crops management in fruit crops.
Identifying harvest dates.
Land suitability assessment for crops.
 Disaster management.
Applications of Remote Sensing in fruit crops
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12. BIOSYSTEMS ENGINEERING 193(2020)115-125
The use of UAVs in monitoring yellow sigatoka in banana
Calou et al (2020)
1-Federal Institute of Education, Science and Technology of Ceara, Iguatu/Varzea Alegre Road, Km 05, Vila
Cajazeiras, 63508-010, Iguatu-CE, Brazil
2-Department of Agricultural Engineering, Federal University of Ceara, FortalezaeCE, 60451-970, Brazil
Case Study 1
Objective
To use high spatial resolution aerial images to monitor the extent of an attack
of yellow sigatoka in a banana crop, following the basic assumptions of
identification, classification, quantification and prediction of phenotypic
factors.
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13. Study area
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14. Flow chart for processing the images obtained with the UAV in the
banana plantation
Russas, Ceara, Brazil
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15. Degree of severity of the attack of yellow sigatoka
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16. Degree of severity of the attack of yellow sigatoka (Mycosphaerella musicola) in field
evaluations (mean values from 30 plants evaluated on each mission). Russas, Ceara,
Brazil.
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17. Symptoms of an attack of yellow sigatoka in banana leaves. Aerial images obtained with the
Inspire 1 UAV, Russas, Ceara, Brazil. 17

18. Overall Accuracy and Kappa Index of the classifier algorithms and percentage values of
the “Abnormal Leaf” class in the results of each classifier algorithm. 18

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20. Enlarged sections of the study area from the flight of September 2017. Russas, Ceara, Brazil.
MX e Maximum Likelihood; MH e Mahalanobis Distance; MD e Minimum Distance; NN e
Artificial Neural Networks; SVM e Support Vector Machine. 20

21. Scientia Horticulture 93 (2002) 105-123
Acreage and production estimation of mango orchard using Indian
Remote Sensing (IRS) Satellite data
Yadav et al (2001)
Indian Institute of Horticulture Research,Hesaraghatta lake post Banglore 560 089, India
Case Study 2
Objective
The objective of this experiment was initiated to estimate acreage and
production of mango orchard using Indian Remote Sensing (IRS) satellite
data. 21

22. SOI topo coverage of the study area (These are 1 *1 maps covering the
study area)
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23. Yield and production estimates of Mango orchard using
IRS data
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25. 25

26. Area/sensor Orchard
type
RS
acreage(ha)
Reported
acreage(ha)
Remarks(RS-
DOH/RS)*100
Total enumeration
technique
Krishna district/IRS 1B
LISS II 1995
Mango 64510 60433 6.32
Krishna district/IRS 1C
LISS III 1996
Mango 69238.679 60433 12.71
Sample segment
approach
Krishna district/IRS 1B
LISS II 1995
Mango 62734.218 60433 3.80
Krishna district/IRS 1C
LISS III 1996
Mango 64805.996 60433 6.74
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27. Case Study-3
Objective
Momona Ethiopian Journal of science
Land Suitability Characterization for Crop and Fruit Production in
Midlands of Tigray, Ethiopia
T.kassa & H.Mulu (2012)
1-Department of Land Resource Management and Environmental Protection,
Mekelle University, P.O. Box: 231, Mekelle (*kassateka@yahoo.com)
2- Wukro St.Mary’s Integrated Rural Development Project, P.O.Box: 12, Wukro
The aim of this evaluation was to find out which parcels of land
may best support the different crops and fruits commonly grown by
the local farmer and recommend these results to the local
stakeholder for an increased yield. 27

28. Study area
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29. Land unit map of the study area
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30. Soil type study area
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31. suitability of soils for agricultural use
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32. Soil chemical characteristics of the Korir Water shed
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33. Land suitability index
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34. Land suitability for the common crops & fruit grown in the study area
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35. Case study-4
Acta Horticulture 1211
Evaluation of specific location of litchi orchards using remote sensing and
GIS techniques: a case study of Muzaffarpur district of Bihar, India
Sinha, S., Singh, R.R., Vimal, B.K. and Siddiqui, Md.W. (2016).
1-Department of Horticulture (Fruit and Fruit Technology), BAC, Sabour-813210, Bhagalpur, India
2-Department of Soil Science and Agricultural Chemistry, BAC, Sabour-813210, Bhagalpur, India
3-Department of Food Science and Postharvest Technology, BAC, Sabour-813210, Bhagalpur, India
The main aim of this research paper is to provide information for litchi grown area using
Remote Sensing-geographic information system (RS-GIS) based techniques in which satellite
image of Indian remote sensing-linear imaging self-scanning (IRS- LISS-III) and normalized
difference vegetation index (NDVI) were used under GIS domain for the analysis of suitable
area and soils
Objective
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40. Small size of land holdings
Lack of success stories or cost-benefits studied
Knowledge and technological gap
Heterogeneity of cropping system in India
Lack of local technical expertise
High initial investment
Challenges of Remote sensing for Horticulture crops in India
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41. • Remote sensing technology has developed from balloon photography to
aerial photography & then advanced to multi-spectral satellite imaging.
• Radiation interaction characteristics of earth and atmosphere in different
regions of electromagnetic spectrum are very useful for identifying and
characterizing earth and atmospheric features.
• For measurements of the Earth's surface and depending on the wavelength
region examined, these properties can include factors such as roughness,
mineralogy, temperature, particle size, and elemental abundance.
• Remote sensing has become a widely accepted research tool for crop
husbandry of almost all the crops grown in the world.
CONCLUSION

42. Future prospects of remote sensing
Till date it is applicable in few perennial
crops. Hence, work should be done in all
horticultural crops in near future.
Precise application of inputs in all the
horticultural crops must be standardize
through remote sensing technology.
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