Optimizing havest schedule of sugarcane crop using genetic algorithm through assimilation of DSSAT-CANEGRO model with remote sensing | |
| Author | Vineeth, Kurapati Penchala |
| Call Number | AIT Thesis no.RS-16-27 |
| Subject(s) | Remote sensing Genetic algorithms Harvesting Production scheduling--Remote sensing Sugarcane |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Remote Sensing and Geographic Information System |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. RS-16-27 |
| Abstract | Crop Simulation Models (CSM‟s) are the primary tools for yield estimation. It provides guidance for taking prominent decisions regarding the factors related to the farm by the decision maker. The yield prediction may be affected as the Crop Simulation Models (CSM‟s) are restricted because of uncertainty in input parameters. To overcome this problem remote sensing techniques are integrated with Crop Simulation Models (CSM‟s). So, that the performance of the model increases. In this study the DSSAT - CANEGRO , is an agro - technology transfer model, is chosen as a medium in order optimize the harvest schedule for different scenarios and specifying the best scenario which would be profitable to both the farmers and industries, in which DSSAT - CANEGRO requires an inputs such as management, soil, weather data here weather data has been selected because, it varies spatially if we collect weather data i.e. temperature, it provides data for the station not for the field, so with the help of remote sensing techniques the maximum and minimum temperatures for different seasons were estimated by creating a regression model and the data obtained by remote sensing techniques is used as an input and finally yield of the crop is obtained by simulating DSSAT model. The maximum and minimum air temperatures estimated for different seasons i.e. rainy, summer and winter having R 2 of 0.789087, 0.746423, 0.773894, 0.703983, 0.701677 and 0.870873 respectively. The productivities obtained by optimizing harvest date for maximum yield; grouping the farms based on distance and defining harvest date for each group; grouping the farms based on distance, planting date and defining harvest date for each group are 3,081,128 Kg, 2,936,684 Kg and 3,059,970 Kg respectively . By comparing t he productivities for each scenario i.e. productivity obtained by grouping farms based on distance is 2,936,684 Kg having a difference of 4.688% when compared with maximum productivity and the productivity obtained by grouping farms based on distance, planting date is 3,059,970 Kg having a difference of 0.686% from maximum productivity condition. These algorithms are essential and beneficial to both the farmers and sugar industry for managing the farm for different cases |
| Year | 2016 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. RS-16-27 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Sarawut Ninsawat |
| Examination Committee(s) | Tripathi, Nitin Kumar;Soni, Peeyush |
| Scholarship Donor(s) | AIT Fellowship |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2016 |