Towards Fine-Scale Yield Prediction of Three Major Crops of India Using Data from Multiple Satellite

Tripathy, Rojalin; Chaudhari, K. N.; Bairagi, G. D.; Pal, Om; Das, Rajesh; Bhattacharya, Bimal K.

doi:10.1007/s12524-021-01361-2

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…The novelty of this study lies in the data fusion approach using parameters from multiple spatial resolution of Geostationary and Lower Earth Orbiting satellites within the basic semi-physical model framework. The study concluded that high resolution remote sensing data would be of immense use for finer scale yield estimation, which can be aggregated at GP and taluka level with satisfactory accuracy (p = 95%)" [85].…”

Section: Moechel Et Almentioning

confidence: 97%

“…Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions". "There is enormous scope and prospective of crop yield prediction at finer scale for farm-level crop management at gram panchayat (GP) level in India" [85]. "Now with the advent of satellite sensors like the MSI from Sentilnel-2 with fine spatial resolution, the possibility of generating frequent information on crop condition at field scale is increasing.…”

Section: Moechel Et Almentioning

confidence: 99%

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Review on Yield Sensing Technologies for Horticultural crops

Lohare

Nair

Sharma

et al. 2023

IJPSS

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The economic production capacity of a farm could be effectively ascertained with the aid of crop map. With the help of yield maps farmer could benefit by increasing the effective placement input management and better map their profitability. While this yield sensing technology is being used for field crops but very few examples are observed for horticultural crops. So, in spite of many conventional technologies new inventions have been done for sensing yields of horticultural crops. This paper presents different review for examining yields that can be bifurcated in Proximal and Space Borne Sensors Sensing System. It gives us a better prediction about the yield of different crops prior to harvest. For yield sensing two methods i.e. Proximal sensing and Space borne sensors sensing system has been discussed in this review paper for crops like Capsicum, Potato, Carrot, Tomato, Melon, Apple, Pear etc. This review also demonstrated the need for more solutions to be explored in future for yield sensing of different crops. Proximal and space borne sensing system based yield sensing technology has been studied in different countries like Chile, Turkey, India, China, USA, UK etc. and their results has been discussed.

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Section: Moechel Et Almentioning

confidence: 97%

Section: Moechel Et Almentioning

confidence: 99%

Review on Yield Sensing Technologies for Horticultural crops

Lohare

Nair

Sharma

et al. 2023

IJPSS

View full text Add to dashboard Cite

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“…In recent years, Indian Space Research Organization (ISRO) has made significant progress using satellite remote sensing applications in agriculture especially generation of agro-met products (Nigam et al, 2021;Bhattacharya, 2007), crop inventory using AI/ML (Das et al, 2021and yield estimation (Bhattacharya et al, 2011, Tripathy et al, 2021 at various scales. With the major development in relation to the atmosphere and climate, improved simulation of tropical cyclone track with regional 3DVar initialization was evaluated using Weather Research and Forecasting (WRF) model.…”

Section: New Technologies For Transforming Agriculturementioning

confidence: 99%

Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies

et al. 2023

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Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. HHaJathrough Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate.

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“…The study captured the yield variability in the farmer's field and opens up avenue for implementing EO-based crop insurance program for Indian farmers. Tripathy et al (2022) demonstrate yield prediction for rice, cotton and wheat using a multi-sensor EO-based approach that uses geostationary INSAT data for insolation, high temporal MODIS for phenology and fAPAR and Sentinel for water scalar and crop mapping.…”

Section: Crop Yield Assessmentmentioning

confidence: 99%

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Ray

Dadhwal

Navalgund

2022

J Indian Soc Remote Sens

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The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982), IRS Utilization Program led (1983( -1995( ), post-IRS-1C launch (1996( -2011 and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings.

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Towards Fine-Scale Yield Prediction of Three Major Crops of India Using Data from Multiple Satellite

Cited by 10 publications

References 38 publications

Review on Yield Sensing Technologies for Horticultural crops

Review on Yield Sensing Technologies for Horticultural crops

Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

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