Delineating the degree of genetic divergence within Assam lemon (Citrus limon ‘Assam lemon’) accessions in Assam, India

Timely and accurate estimation of acreage and production of horticulture crops is necessary for deciding how much, where and when to export these commodities in the national and global markets. Remote sensing has been one of the methods adopted, in addition to conventional sampling methods, for improving the estimates. Parametric image classification algorithms have been used by many researchers for identification and area estimation of horticulture crops. But these algorithms result in several unclassified pixels leading to over/underestimates. This study has been undertaken to estimate the area of two horticulture crops (i.e., mango and coconut) of Hesaraghatta hobli of Bengaluru urban district using Convolutional Neural Network (CNN) on Google Colaband Random Forest (RF) algorithms on Google Earth Engine (GEE). Remotely sensed data acquired by the Multi-Spectral Instrument (MSI) onboard Sentiel-2A satellite was used. Spectral signatures of horticulture crops and other associated cover types have been generated to identify the cover types and for selecting appropriate band combinations. Two different band combinations were used for area estimation of selected horticulture crops: i) Near-InfraRed (NIR), Red, and Green all three having a spatial resolution of 10 m, ii) Red edge-3, Short-Wave InfraRed1 (SWIR1) and Short-Wave InfraRed2 (SWIR2) having 20 m spatial resolution. Area estimates of horticulture crops and associated cover types were validated with respect to ground truth and statistical reports from Karnataka State Directorate of Horticulture (KSDH). It was found that the CNN model performed better than RF using NIR, Red, and Green band combination with an overall accuracy of 84%, but it failed to give similar accuracies with Red edge 3, SWIR1, and SWIR2 band combination. We attempted transfer learning using the trained CNN model at two different study areas far away from the study area and found encouraging results.

Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka

The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969–1982), 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. 

/pdf/progress-and-challenges-in-earth-observation-data-2jv2iuqp.pdf

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

Applications of Advanced Geospatial Technology for Expansion of Area under Horticultural Crops in North Eastern Region of India

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Applications of Advanced Geospatial Technology for Expansion of Area under Horticultural Crops in North Eastern Region of India