Investigation of the potential for precision soil and crop growth mapping to improve tuber size distribution at harvest

Control of tuber size distribution (TSD) in potatoes is important for maximising profits. TSD and its spatial variability are related to stem density variation. This study demonstrated the potential to map stem density in a field using high throughput methods. The potential of satellite image time series in modelling stem density and yield was also examined. Sentinel-2 satellite data was used to create spectral signatures of potato plants and their temporal evolution. Features engineered from this data were able to model potato Marketable yield and stem density. The study uncovered high potential for crop growth mapping to predict TSD and aid in decision-support system. Understanding the variability of soil nutrients and their effects on TSD can also help in the delineation of management zones for precision applications like variable rate fertilization. In this study, a method for quantifying TSD based on the Weibull distribution was proposed, with consistently lower Root Mean Square Error than currently prevalent methods. With this method, negative relationships between TSD and excess soil nutrients were uncovered.

Papers

Mhango J, Hartley W, Harris W,  Monaghan J (2021). Comparison of potato (Solanum tuberosum L.) tuber size distribution fitting methods and evaluation of the relationship between soil properties and estimated distribution parameters. The Journal of Agricultural Science, 159(9-10):643-657. (doi:10.1017/S0021859621000952)

Mhango JK, Harris WE, Monaghan JM (2021) Relationships between the Spatio-Temporal Variation in Reflectance Data from the Sentinel-2 Satellite and Potato (Solanum tuberosum L.) Yield and Stem Density. Remote Sensing 13(21):4371 (doi.org/10.3390/rs13214371)

Mhango JK, Grove IG, Hartley W, Harris EW, Monaghan JM (2021) Applying Colour-Based Feature Extraction and Transfer Learning to Develop a High Throughput Inference System for Potato (Solanum tuberosum L.) Stems with Images from Unmanned Aerial Vehicles after Canopy Consolidation. Precision Agriculture  23:643–669 (doi.org/10.1007/s11119-021-09853-4)

Mhango JK, Harris EW, Green R, Monaghan JM (2021) Mapping Potato Plant Density Variation Using Deep Learning and Unmanned Aerial Vehicles for Precision Agriculture. Remote Sensing 13(14): 2705 (doi.org/10.3390/rs13142705)