APPLICATION OF REMOTE SENSING IN FRUIT PRODUCTION.pptx

Remote sensing plays a crucial role in the advancement of Agriculture.

1. APPLICATION OF REMOTE SENSING IN FRUIT PRODUCTION PRESENTED BY – SNIGDHA NAG FINAL YEAR M.Sc.(Ag) FRUIT SCIENCE ADMISSION NO. – 211221906 COLLEGE OF AGRICULTURE, ODISHA UNIVERSITY OF AGRICULTURE & TECHNOLOGY,BHUBANESWAR 1 MASTER’S CREDIT SEMINAR FSC-591(1+0)

2. Content Introduction History Components Principle Elements Application Research Findings Govt. initiatives Challenges Conclusion Future Thrust References 2

3. Introduction Remote sensing is the science and art of acquiring information about an object, area or phenomena through the analysis of data obtained by a device that is not in contact with the target under investigation (Pal et al., 2022). Remote – Not in contact with an object Sensing – Getting information 3

4. Indian space research organization (ISRO) established by India's first Prime Minister Jawaharlal Nehru and scientist Vikram Sarabhai in 1969. • ISRO’s mission is to make space technology accessible for the benefit of the common man and to the nation. • ISRO maintains one of the largest fleet of remote sensing satellites. National Remote Sensing Agency (NRSA) was established in 1974 and registered as a society under the Dept. of Science & Technology. Currently, it is known as Nation Remote Sensing Centre (NRSC) situated at Hyderabad. 4 History

5. III. Indian space research organization (ISRO) launched Bhaskar-I & Bhaskar-II in 1979 & 1981 respectively . IV. Indian Remote Sensing Satellite (IRS-1A) was launched in 17 th March, 1988 by ISRO. V. Earth Observation Satellite-04 (EOS-04) was launched in 14 th February 2022. 5 History Bhaskar-I Bhaskar-II IRS-1A

6. Components of Remote Sensing 6

7. Platform For remote sensing applications, sensors should be mounted on suitable stable platforms. These platforms can be :- ground based, air borne or space borne based 7 Lechner et. al., 2020

8. Sensors  S ensors are tools or devices used to gather information about things that happen on earth (like earthquakes, volcanic eruption, forest fires) or characteristics of the earth's atmosphere (like temperature, humidity, air pressure). Sensors can be installed on the ground as well as carried aboard on satellites or aircrafts . Sensors are classified into 2 parts:- Based on energy sources Based on region of electromagnetic spectrum 8

9. Classification of Remote Sensing sensors Based On Energy Sources- 9 Passive Sensors Active Sensors Sensors detect the reflected / emitted electromagnetic radiation from natural sources such as sun and earth thermal emission. Sensors detect reflected response from the objects that irradiated from artificially generated energy sources such as radar and lidar.

10. II. Based on region of electromagnetic spectrum- 10

11. Fig.1- Electromagnetic Spectrum 11 Mclaughlin & Ballance (2012)

12. Principle of Remote Sensing Detecting and recording radiant energy reflected or emitted by objects or surface. 12

13. Fig.2- Working Principle Of Remote Sensing 13

14. Application of Remote Sensing in fruit crops 14

15. 15

16. CASE STUDY:1 16 OBJECTIVE- Acreage estimation of mango orchard using hyperspectral satellite data. Uttar Pradesh, India Paul et al., 2018 Acreage Estimation Of Mango Orchards Using Hyperspectral Satellite Data

17. Methodology 17

18. (a) (b) Fig.4- Location of 24 mango orchards on (a) Google image (b) Hyperion image of study area 18 Paul et al., 2018

19. Fig.5- Bad bands from EO-1 Hyperion image of Meerut. Fig.6- FLAASH module for atmospheric correction. 19 Paul et al., 2018

20. Fig.7- Coordinates of mango orchards identified in the image 20 Paul et al., 2018

21. 21 Paul et al., 2018

22. Result The hyperion hyperspectral satellite data was evaluated to estimate the area under all mango orchards. These estimates were compared with actual area under mango orchards measured using Global Positioning System (GPS). The total area under mango was predicted as 961.88 ha which was 92% close to ground data 889.65 ha. 22

23. CASE STUDY-2 23 Hyperspectral Remote Sensing For Temperate Hortiulture Fruit Crops In Northern-western Himalayan Region OBJECTIVE- To highlight the need of a digital spectral library for horticulture. To identify the disease spread on a particular crop in a region. To develop a model for the estimation of biophysical and biochemical parameter that may be helpful for monitoring long time climatic changes in horticulture crops. Upadhyay et al., 2019 Uttarakhand, India

24. METHODOLOGY A combined methodology has been applied for the following- Processing of hyperspectral data for generation of spectral library. Identification of diseases. Development of model for the estimation of biophysical and biochemical properties of temperate horticulture fruit crops. 24

25. Generation Of Spectral Library 25 Poorly calibrated vertical bad lines FLAASH PCA &MNF

26. Identification Of Disease & Development Of Model 26 Spectroradiometer based ground sample collection Existing Models for Biophysical and Biochemical Parameter Identification of Diseases

27. Fig.7- Hyperspectral cubes of different terrain conditions & sample spectral plots for different features (https://www.isro.gov.in). 27 Upadhyay et al., 2019

28. Result Encourage researchers to apply hyperspectral remote sensing for horticulture crop . It is an important tool for discriminating or mapping the specific land cover among the various existing classes. 28

29. CASE STUDY-3 29 Arid Fruit Crops Suitability Evaluation For Drought Mitigation Using Remote Sensing & Gis Techniques In South Deccan Plateau, Andhra Pradesh, India OBJECTIVE- To evaluate soil-site suitability for five fruit crops (mango, amla, jamun, custard apple and ber ) in Inagaluru panchayat, Anantapur district of Andhra Pradesh for mitigate the drought and improve the farmer’s livelihood. Srinivasan et al ., 2020 Andhra Pradesh, India

30. Soil Characteristics & Limitations Based on the results of the study area, it is concluded that the soils of Inagaluru panchayat was moderately shallow to deep in depth, slightly acidic to moderately alkaline in reaction, non-saline low to high in organic carbon, CEC and base saturation. Result of suitability evaluation for five fruit crops with major and minor limitations are high gravel content, low soil depth, poor soil fertility, undulated topography heavy soil texture. 30

31. Fig.8- Map of Land Suitability evaluation for Mango 31 Suitable Classes For Soil Area of Mango in ha S1- Highly suitable S2- Moderately suitable 531 S3- Marginally suitable 1065 N1- Currently not Suitable 1019 N2- Permanently not suitable 178 Table.1- Distribution of Mango in different suitability classes of soil. Srinivasan et al., 2020

32. Fig.9- Map of Land Suitability evaluation for Amla 32 Suitable classes for soil Area of Amla in ha S1- Highly suitable S2- Moderately suitable 1520 S3- Marginally suitable 997 N1- Currently not suitable 98 N2- Permanently not suitable 178 Table.2- Distribution of Amla in different suitability classes of soil. Srinivasan et al., 2020

33. Fig.10- Map of Land Suitability evaluation for Jamun 33 Suitable classes for soil Area of Jamun in ha S1- Highly suitable S2- Moderately suitable 531 S3- Marginally suitable 1499 N1- Currently not suitable 584 N2- Permanently not suitable 178 Table.3- Distribution of Jamun in different suitability classes of soil. Srinivasan et al., 2020

34. Fig.11- Map of Land Suitability evaluation for Custard Apple 34 Suitable classes for soil Area of Custard apple in ha S1- Highly suitable 96 S2- Moderately suitable 1855 S3- Marginally suitable 665 N1- Currently not suitable N2- Permanently not suitable 178 Table.4- Distribution of Custard Apple in different suitability classes of soil. Srinivasan et al., 2020

35. Fig.12- Map of Land Suitability evaluation for Ber 35 Suitable classes for soil Area of Ber in ha S1- Highly suitable 538 S2- Moderately suitable 1511 S3- Marginally suitable 566 N1- Currently not suitable N2- Permanently not suitable 178 Table.5- Distribution of Ber in different suitability classes of soil. Srinivasan et al., 2020

36. Result Assessment of site specific soil constraints for fruit crops (mango, amla, jamun, custard apple and ber ) cultivation These assessments can be used to implement better technologies for soil and water conservation practices. 36

37. CASE STUDY-4 37 Horticulture fruit crops mapping of Adampur & Hisar-2 nd block of Hisar District using Geo-informatics techniques OBJECTIVE- Mapping of horticulture site in the given study area using WORLD VIEW-2 & IRS LISS III data. Mapping of horticulture fruit crops using IRS-P6 LISS-III data adopting Digital Analysis Approach. Veena(2012) Haryana, India

38. 38 Horticulture fruit crops area Area in ha (DOH) Area in ha (RS) %RD (SVI approach) Adampur 620.70 506.23 -18.44 Hisar-ii 450.81 445.88 -1.093 Total 1071.51 952.11 -11.14 Horticulture fruit crops area Area in ha (DOH) Area in ha (RS) % RD (HYBRID approach) Adampur 620.70 413.12 -33.44 Hisar-ii 450.81 298.23 -33.84 Total 1071.51 711.35 -33.61 Table 6 a. & b.-Horticulture Fruit Crops Area in Different Blocks of Hisar District a. b. Veena(2012)

39. 39 Area in ha (RS) Horticulture Crops 310.07 Young Crops 642.04 Mature Crops 952.11 Total Area Table.7- Classification of horticultural fruit crops based on age group Veena (2012)

40. Result It was revealed from the study that the total area under horticultural fruit crops(citrus, guava, kinnow orange and anola ) was 506.23 hectares in Adampur block and 445.88 hectares in Hisar II block, with a total area of 952.11 hectares. Out of total area of 952.11 hectares, 310.07 hectare has young crops and 642.04 hectare has mature crops. 40

41. CASE STUDY-5 41 Soil fertility maps preparation using GPS and GIS in Dhenkanal District, Odisha, India OBJECTIVE- To create maps that depict the fertility levels of soils in Dhenkanal District. Mishra et al., 2014 Odisha, India

42. 42 Sl. No. Block name/ No. of samples collected RANGE MEAN RANGE MEAN RANGE MEAN 1 KANKADAHAD(54) 4.48-5.80 4.88 0.036-0.231 0.082 0.336-1.464 0.837 2 BHUBAN(60) 4.61-7.21 5.24 0.027-0.235 0.088 0.118-1.197 0.685 3 KAMAKHYNAGAR(66) 4.33-6.33 5.26 0.024-0.236 0.080 0.063-2.19 0.724 4 PARJANG(60) 4.63-7.42 5.38 0.032-0.248 0.098 0.118-1.929 0.751 5 ODAPADA(78) 4.52-7.83 5.66 0.048-0.592 0.182 0.084-1.785 0.822 6 HINDOL(96) 4.68-7.72 5.85 0.028-0.465 0.094 0.201-1.812 0.707 7 DHENKANAL SARDAR(84) 4.59-6.81 5.25 0.016-0.402 0.117 0.233-1.634 0.640 8 GANDIA(96) 4.41-6.56 5.14 0.032-0.159 0.090 0.326-1.255 0.756 pH EC ( dS /m ) OC(%) Table.8- Soil pH, EC and Organic Carbon of Dhenkanal District Mishra et al., 2014

43. 43 Fig.13- Soil pH map of Dhenkanal District (GPS and GIS based ) Fig.14- Soil organic carbon content map of Dhenkanal District (GPS and GIS based ) Mishra et al., 2014

44. 44 Sl. No. Block name/ No. of samples collected RANGE MEAN RANGE MEAN RANGE MEAN 1 KANKADAHAD(54) 81.25-396.25 186.00 0.245-14.2 6.51 66.08-293.44 146.45 2 BHUBAN(60) 113.75-281.25 167.54 0.735-28.66 7.38 29.12-300.16 104.84 3 KAMAKHYNAGAR(66) 105.00-247.50 163.48 0.245-20.09 4.42 49.28-434.56 187.60 4 PARJANG(60) 87.50-336.75 157.86 0.245-17.88 7.00 70.56-840.0 318.56 5 ODAPADA(78) 107.50-342.50 177.58 0.245-39.69 8.32 73.92-1004.64 383.82 6 HINDOL(96) 71.25-252.50 162.92 0.245-26.95 6.39 44.8-440.16 203.53 7 DHENKANAL SARDAR(84) 105.00-365.00 170.47 0.49-28.17 5.35 26.80-831.04 216.94 8 GANDIA(96) 97.50-242.50 166.18 0.49-50.71 9.09 66.8-538.72 248.51 Av. N (kg ha-1) Av. P (kg ha-1) Av. K (kg ha-1) Table.9- Nutrients content of Dhenkanal district Mishra et al., 2014

45. 45 Fig.15- Soil available nitrogen content map of Dhenkanal District (GPS and GIS based ) Fig.16- Soil available phosphorus content map of Dhenkanal District (GPS and GIS based ) Mishra et al., 2014

46. 46 Fig.17- Available potassium content of soil in Dhenkanal District (GPS and GIS based ) Mishra et al., 2014

47. 47 SL. No. Block name/ No. of samples collected RANGE MEAN RANGE MEAN 1 KANKADAHAD(54) 4.55-39.90 16.33 0.41-1.47 0.884 2 BHUBAN(60) 5.25-28.00 12.38 0.27-0.92 0.556 3 KAMAKHYNAGAR(66) 2.45-42.35 14.46 0.23-0.82 0.440 4 PARJANG(60) 2.30-69.65 17.80 0.14-0.69 0.378 5 ODAPADA(78) 3.85-51.10 15.70 0.18-0.87 0.460 6 HINDOL(96) 2.45-83.65 14.40 0.13-1.33 0.597 7 DHENKANAL SARDAR(84) 2.80-32.10 12.96 0.18-0.64 0.375 8 GANDIA(96) 0.70-34.30 16.35 0.13-0.92 0.454 Sulphur (mg kg-1 ) Boron (mg kg-1 ) Mishra et al., 2014 Table.3- Available sulfur and boron content of Dhenkanal

48. 48 Fig.18- Soil available sulfur content map of Dhenkanal District (GPS and GIS based ) Fig.19- Soil boron content map of Dhenkanal District ( GPS and GIS based ) Mishra et al., 2014

49. Result The soils of all blocks were acidic in nature and hence there is a need of soil reclamation. Application of 1 to 2 ton / ha of papermill sludge is recommended to correct soil acidity . Available nitrogen and phosphorus content were low and potassium content varied from low to high. Application of mixture of water soluble and insoluble phosphatic fertilizers recommended to increase the crop yield. The available sulfur was found to be medium. Hence, application of phosphogypsum @ 200 kg/ha is recommended to enhance the crop productivity. Boron content remained below the critical limits except in Kankadahad , Bhuban and Hindol blocks. Hence, soil application of boron is suggested. 49

50. 50 GOVERNMENT INITIATIVES

51. In 1988, Government of India started a project called “Crop Acreage and Production Estimation (CAPE)” to collect the statistics of agricultural output. This project lacks forecast of crops at sowing stage for which weather data and information of economic factors are required. In 2006, a new program was launched called “ Forecasting agricultural output using space, agro -meteorological and land-based observations” (FASAL). The main o bjective is to combine all the information gathered from different sources, including land-based data and remote sensing inputs, to come up with timely crop forecasts. A team of ISRO, State Remote Sensing Applications Centers, State Agricultural Universities and many other institutions are working on FASAL. The remote sensing component of FASAL is being handled by the Ahmedabad-based Space Application Centre of ISRO. 51 FASAL

52. NADAMS National Remote Sensing Centre, Hyderabad, is operating another project called “ National Agricultural Drought Assessment and Monitoring System ” (NADAMS). NADAMS is providing assessment of agricultural drought at district level for 9 states and sub district level for 4 states, in terms of prevalence, severity and persistence, during kharif season (June-Nov) It is also responsible for submission of monthly drought reports to the Ministry of Agriculture and State Departments of Agriculture and Relief of different states. The methodology essentially reflects the integration of satellite derived crop condition or surface wetness with ground collected rainfall and crop area progression to make decisions regarding the prevalence, intensity and persistence of agricultural drought situation. 52 Seshasai et al., 2016

53. NCFC FASAL & NADAMS have now been merged and put under the control of National Crop Forecast Centre (NCFC) in April 2012. NCFC is an integrated center which will provide estimates of crop output and assess the drought situation in the country through latest technologies. The NCFC is set to begin issuing crop forecasts and drought situation reports from the ensuing kharif season for 11 major crops. More crops are planned to be taken up subsequently. 53

54. CHALLENGES 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 54

55. Conclusion Remote sensing has emerged as a powerful tool in the realm of fruit production. Through the use of satellites, drones, and other technologies, it enables farmers and researchers to monitor orchards and vineyards with precision and efficiency. From assessing crop health to optimizing irrigation and pest management. Remote sensing contributes significantly to increased yields, reduced resource usage, and improved sustainability in fruit production. Progressive Indian farmers, with guidance from the public and private sectors, and agricultural associations, will adopt it in a limited scale as the technology shows potential for raising yields and economic returns on fields with significant variability, and for minimizing environmental degradation. 55

56. Future Thrust It can be adaptable to all horticultural crops. Application of inputs can be standardized in a precise manner. The advancement in remote sensing technologies:- Analyse a larger area than earlier. Wide range of spatial and spectral resolution. Biophysical parameters (LAI & Biomass) data derived from high-resolution satellites can be integrated along with the crop growth model. Use of drones or unmanned aerial vehicle ( UAV) made it possible to get a better resolution in images than those satellite images. 56

57. References Pal S, Pandey SK and Sharma SK. 2022. Applications of remote sensing and GIS in fruit crops: A review . The Pharma Innovation Journal 2022; SP-11(2): 186-191. Lechner AM, Foody GM and Boyd DS. 2020. Applications in Remote Sensing to Forest Ecology and Management. One Earth. 2. 405-412. 10.1016/j.oneear.2020.05.001. Mclaughlin B & Ballance CP. 2012. Photoionization, fluorescence, and inner-shell processes. Brendan Michael Mclaughlin on 26 May 2014. https://www.researchgate.net/publication/230569765. Veena. 2012. Horticulture Fruit Crops Mapping of Adampur and Hisar- IInd Blocks of Hisar District Using Geo-informatics Techniques. International Journal of Science and Research (IJSR) ,ISSN (Online): 2319-7064 ,Impact Factor (2012): 3.358. Seshasai M, Murthy CS, Chandrasekar K and Jeyaseelan AT. 2016. Agricultural drought: Assessment & monitoring. Mausam. 67. 10.54302/mausam.v67i1.1155. 57

58. References Paul NC, Sahoo PM, Ahmad T, Sahoo RN, Krishna G and Lal SB. 2018. Acreage estimation of mango orchards using hyperspectral satellite data . Indian J. Hort. 75(1), March 2018: 27-33. Upadhyay P, Uniyal D and Bisht MPS. 2019. Hyperspectral Remote Sensing for Temperate Horticulture Fruit crops in Northern-Western Himalayan Region : A Review. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 . ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India. Srinivasan R, Hegde R, Srinivas S, Niranjan KV and Maddileti N. 2020. Arid Fruit Crops Suitability Evaluation for Drought Mitigation Using Remote Sensing and GIS Techniques in South Deccan Plateau, Andhra Pradesh, India . Climate Change and Environmental Sustainability 2020, 8(1):13-23 DOI: 10.5958/2320-642X .2020.00002.2. Mishra A, Pattnaik T, Das D and Das M. 2014. Soil Fertility Maps Preparation Using GPS and GIS in Dhenkanal District, Odisha, India. International Journal of Plant & Soil Science 3(8): 986-994 , 2014; Article no. IJPSS.2014.8.005. 58

59. 59