Nowadays, modern agriculture has benefited from Precision Agriculture (PA) through incorporation of technological advances like the use of GIS (Geographic Information Systems), RS (Remote Sensing), GPS (Global Positioning System) and advanced information processing. Based on the GIS, RS and GPS, a study was conducted to develop a cropland map using GIS and remotely sensed unsupervised algorithm for suitability analysis of paddy harvester. The research was carried out at four selected locations such as Kulbaria-Baratia, Mundopasha, Charwapda, and Holdibaria villages of Dumuria, Wazirpur, Subarnachar and Kalapara upazilas of Khulna, Barishal, Noakhali Patuakhali districts, respectively in the southern Bangladesh. The satellite images for GIS mapping were captured at vegetation stage of Boro-2018 and Aman-2018 during March-April and October-November. Technical performances of reaper and combine harvester were used to determine the required number harvester based on the estimated cultivated area found through GIS maps. The calculated required number of a) reaper, b) mini combine and c) medium combine to cover the estimated paddy area are a) 17 and 16, 1 and 5, 38 and 127, 6 and 21, b) 35 and 32, 3 and 10, 76 and 254, 13 and, 42 and c) 10 and 9, 1 and 3, 21 and 72, 4, and 12 during Boro and Aman seasons at Kulbaria-Baratia, Mundopasha, Charwabda and Holdibaria of Dumuria, Wazirpur, Subarnachar and Kalapara upazilas, respectively. The estimated results revealed that GIS tools and remote sensing are helping in simplification and visualization by incorporating data sets which can supports decision making for the implementation of paddy harvesting technologies in order to ensure the proper agricultural mechanization. Based on the accuracy assessment, GIS tool is found very useful to assess area to be harvested mechanically with specific type and number of harvester. It can be considered for formulating mechanized harvesting policy through further research in other areas.
Key words: Precision agriculture, cropland mapping, GIS, remote sensing, LULC classification, paddy harvesting technologies.
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