Field path optimization to reduce headland and turning maneuvers at regional scales: automated detection of cultivation direction in the state of Brandenburg, Germany
Path planning for optimized field-work pattern is an important task within precision farming. The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a n...
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| Veröffentlicht in: | Precision agriculture 2023-10, Vol.24 (5), p.2126-2147 |
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| description | Path planning for optimized field-work pattern is an important task within precision farming. The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a new method for automated detection of the current cultivation direction of several thousands of agricultural fields and compared the current cultivation direction with an optimized cultivation direction generated from a path planning algorithm. Airborne imagery from 2019 was analyzed using a modified Gabor filter. The identification takes place on a sub-plot level and can therefore detect small-scale differences in cultivation direction within fields. The method for identification of current cultivation direction had a high success rate of 87.5%. Fields with a high potential to save turning maneuvers and to reduce the area of headland were identified. From 3410 fields, a total of 58162 turning maneuvers and 507 ha headland were saved. This corresponds to 14.1% of all turning maneuvers and 7.6% of the total headland area for all analyzed fields in Brandenburg. A high optimization potential was demonstrated for field paths when efficient processing directions are taken into account. The method can be extended to the analysis of satellite imagery and thus offers the possibility of identifying current cultivation directions with a high spatial and temporal resolution. In future, this knowledge can be embedded within decision support systems for real-time optimization of field machinery path planning to support sustainable cropping practices. |
| doi_str_mv | 10.1007/s11119-023-10033-9 |
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The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a new method for automated detection of the current cultivation direction of several thousands of agricultural fields and compared the current cultivation direction with an optimized cultivation direction generated from a path planning algorithm. Airborne imagery from 2019 was analyzed using a modified Gabor filter. The identification takes place on a sub-plot level and can therefore detect small-scale differences in cultivation direction within fields. The method for identification of current cultivation direction had a high success rate of 87.5%. Fields with a high potential to save turning maneuvers and to reduce the area of headland were identified. From 3410 fields, a total of 58162 turning maneuvers and 507 ha headland were saved. This corresponds to 14.1% of all turning maneuvers and 7.6% of the total headland area for all analyzed fields in Brandenburg. A high optimization potential was demonstrated for field paths when efficient processing directions are taken into account. The method can be extended to the analysis of satellite imagery and thus offers the possibility of identifying current cultivation directions with a high spatial and temporal resolution. In future, this knowledge can be embedded within decision support systems for real-time optimization of field machinery path planning to support sustainable cropping practices.</description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1007/s11119-023-10033-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Agricultural land ; Agriculture ; Algorithms ; Artificial intelligence ; Atmospheric Sciences ; Automation ; Biomedical and Life Sciences ; Chemistry and Earth Sciences ; Computer Science ; Cultivation ; Decision support systems ; energy ; Energy consumption ; Gabor filters ; Germany ; Headlands ; Life Sciences ; Maneuvers ; Mental task performance ; Optimization ; Path planning ; Physics ; precision ; Precision farming ; remote sensing ; Remote Sensing/Photogrammetry ; Satellite imagery ; Soil compaction ; Soil Science & Conservation ; Statistics for Engineering ; Sustainable agriculture ; Temporal resolution</subject><ispartof>Precision agriculture, 2023-10, Vol.24 (5), p.2126-2147</ispartof><rights>The Author(s) 2023</rights><rights>The Author(s) 2023. 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The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a new method for automated detection of the current cultivation direction of several thousands of agricultural fields and compared the current cultivation direction with an optimized cultivation direction generated from a path planning algorithm. Airborne imagery from 2019 was analyzed using a modified Gabor filter. The identification takes place on a sub-plot level and can therefore detect small-scale differences in cultivation direction within fields. The method for identification of current cultivation direction had a high success rate of 87.5%. Fields with a high potential to save turning maneuvers and to reduce the area of headland were identified. From 3410 fields, a total of 58162 turning maneuvers and 507 ha headland were saved. This corresponds to 14.1% of all turning maneuvers and 7.6% of the total headland area for all analyzed fields in Brandenburg. A high optimization potential was demonstrated for field paths when efficient processing directions are taken into account. The method can be extended to the analysis of satellite imagery and thus offers the possibility of identifying current cultivation directions with a high spatial and temporal resolution. 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The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a new method for automated detection of the current cultivation direction of several thousands of agricultural fields and compared the current cultivation direction with an optimized cultivation direction generated from a path planning algorithm. Airborne imagery from 2019 was analyzed using a modified Gabor filter. The identification takes place on a sub-plot level and can therefore detect small-scale differences in cultivation direction within fields. The method for identification of current cultivation direction had a high success rate of 87.5%. Fields with a high potential to save turning maneuvers and to reduce the area of headland were identified. From 3410 fields, a total of 58162 turning maneuvers and 507 ha headland were saved. This corresponds to 14.1% of all turning maneuvers and 7.6% of the total headland area for all analyzed fields in Brandenburg. A high optimization potential was demonstrated for field paths when efficient processing directions are taken into account. The method can be extended to the analysis of satellite imagery and thus offers the possibility of identifying current cultivation directions with a high spatial and temporal resolution. In future, this knowledge can be embedded within decision support systems for real-time optimization of field machinery path planning to support sustainable cropping practices.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11119-023-10033-9</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0003-4254-6055</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural land Agriculture Algorithms Artificial intelligence Atmospheric Sciences Automation Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Cultivation Decision support systems energy Energy consumption Gabor filters Germany Headlands Life Sciences Maneuvers Mental task performance Optimization Path planning Physics precision Precision farming remote sensing Remote Sensing/Photogrammetry Satellite imagery Soil compaction Soil Science & Conservation Statistics for Engineering Sustainable agriculture Temporal resolution |
| title | Field path optimization to reduce headland and turning maneuvers at regional scales: automated detection of cultivation direction in the state of Brandenburg, Germany |
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