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An integrated data-driven approach to monitor and estimate plant-scale growth using UAV

UAV-mounted sensors can be used to estimate crop biophysical traits, offering an alternative to traditional field scouting. However, the high temporal resolution offered by UAV platforms, critical for identifying small differences in crop conditions, is rarely exploited throughout the entire growing season. This limits growers' ability to obtain timely information for real-time interventions. New findings support that it is possible to parametrize an entire crop growth cycle under different conditions by accumulating sufficient data over time and using logistic growth models to highlight growth patterns. A step forward would be to model crop growth cycle at the plant-level in order to anticipate the optimal harvest dates in each plot or quickly identify growth problematics. Individual plant monitoring can be achieved by combining high spatial resolution images with accurate segmentation algorithms. The main objective of the study was therefore to develop and validate an integrated pipeline based on multidimensional data to extract predictive growth metrics for crop monitoring at the plant-level under various field conditions. The plant growth monitoring workflow was based on a three-step design ultimately leading to decision-making and reporting. Lettuce (Lactuca sativa L.) was chosen as a model plant due to its simple geometry, rapid growth and simple cultivation method. Treatments were composed of contrasting cover crops. Overall, correlation analysis showed that UAV-derived morphological metrics are reliable proxies for harvested biomass throughout the growing season, especially in later stages (Spearman's ρ > 0.9) and can be used as growth indicators. Therefore, Logistic Growth Curves (LGCs) were fitted to Crop Object Area (COA) values for each individual lettuce, using data up to 26 (generating G26 LGCs), 30 (G30) and 37 (G37) Days After Transplant (DAT). To assess the quality of their projections, G26 and G30 were compared to the reference LGC G37. The results indicated that Mean Absolute Percentage Error (MAPE) of projected COA was 9.6% and 6.8% for G26 and G30 respectively. Overall, the LGC parameters were close to the reference and highly correlated with the harvested biomass. The study also demonstrated the potential of having very good insight on plant maturity level by modeling the LGC 13 days before harvest. Furthermore, a dashboard was proposed to monitor current and projected maturity level, highlighting areas for further investigation. This novel integrated pipeline has the potential to become a valuable tool for research, on-farm decision making, and field interventions by providing data on plant biomass, maturity, and growth stages under different conditions, used as crop growth indicators.

Ketersediaan
47621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier Science Publisher.,
Deskripsi Fisik
13 hlm PDF, 10.547 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.11, January 2024
Subjek
UAV
Crop monitoring
Plant scale growth model
Logistic growth curve
Growth characteristics
Maturity estimation
Lettuce
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • An integrated data-driven approach to monitor and estimate plant-scale growth using UAV
    UAV-mounted sensors can be used to estimate crop biophysical traits, offering an alternative to traditional field scouting. However, the high temporal resolution offered by UAV platforms, critical for identifying small differences in crop conditions, is rarely exploited throughout the entire growing season. This limits growers' ability to obtain timely information for real-time interventions. New findings support that it is possible to parametrize an entire crop growth cycle under different conditions by accumulating sufficient data over time and using logistic growth models to highlight growth patterns. A step forward would be to model crop growth cycle at the plant-level in order to anticipate the optimal harvest dates in each plot or quickly identify growth problematics. Individual plant monitoring can be achieved by combining high spatial resolution images with accurate segmentation algorithms. The main objective of the study was therefore to develop and validate an integrated pipeline based on multidimensional data to extract predictive growth metrics for crop monitoring at the plant-level under various field conditions. The plant growth monitoring workflow was based on a three-step design ultimately leading to decision-making and reporting. Lettuce (Lactuca sativa L.) was chosen as a model plant due to its simple geometry, rapid growth and simple cultivation method. Treatments were composed of contrasting cover crops. Overall, correlation analysis showed that UAV-derived morphological metrics are reliable proxies for harvested biomass throughout the growing season, especially in later stages (Spearman's ρ > 0.9) and can be used as growth indicators. Therefore, Logistic Growth Curves (LGCs) were fitted to Crop Object Area (COA) values for each individual lettuce, using data up to 26 (generating G26 LGCs), 30 (G30) and 37 (G37) Days After Transplant (DAT). To assess the quality of their projections, G26 and G30 were compared to the reference LGC G37. The results indicated that Mean Absolute Percentage Error (MAPE) of projected COA was 9.6% and 6.8% for G26 and G30 respectively. Overall, the LGC parameters were close to the reference and highly correlated with the harvested biomass. The study also demonstrated the potential of having very good insight on plant maturity level by modeling the LGC 13 days before harvest. Furthermore, a dashboard was proposed to monitor current and projected maturity level, highlighting areas for further investigation. This novel integrated pipeline has the potential to become a valuable tool for research, on-farm decision making, and field interventions by providing data on plant biomass, maturity, and growth stages under different conditions, used as crop growth indicators.
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