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Project properties |
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| Title | A machine vision/learning approach for yield estimation in a wide spinach (Spinacia oleracea) field |
| Group | Information Technology Group |
| Project type | thesis |
| Credits | 24-39 |
| Supervisor(s) | João Valente |
| Examiner(s) | João Valente |
| Contact info | João Valente (joao.valente@wur.nl) |
| Begin date | 2020/05/01 |
| End date | 2024/08/31 |
| Description | Unmanned aerial vehicles (UAV) shipped with on-board sensors have become an effective remote sensing (RS) tool in agriculture. They have been used mainly for image surveying and there are still many helpful aerial RS application to develop. Working with UAV it’s an added valuable point for any professional who wishes to succeed in this competitive market. Herein, you will have the opportunity to work in a novel and ambitious project with UAV applied to precision agriculture.
Crop producers must monitor fields daily to address many laborious tasks. Some of this task consists in looking for potential high-quality germinated plant candidates, determine when is ready to be harvest, and predict the crop yield. Because fields are wide and the number of resources is often limited, the assessments are not eminent and take a considerable amount of time (> 1 day). |
| Used skills | The goal of this work is to use computer visions and machine learning approaches for crop yield estimation through high-resolution UAV imagery. This thesis will be carry out in the follow steps: a) Review previous works in machine vision and learning techniques applied to agriculture; b) Design an approach to solve this problematic; c) Experiments with the already available dataset. |
| Requirements | UAV enthusiast, Willing for learning novel software and hardware tools, Excited to work in robotics
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