Abstract
Abstract. Biomass and yield are important variables used for assessing agricultural production. However, these variables are difficult to estimate for individual plants at the farm scale and may be affected by abiotic stressors such as salinity. In this study, the wild tomato species, Solanum pimpinellifolium, was evaluated through field and UAV-based assessment of 600 control and 600 salt-treated plants. The aim of this research was to determine, if UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest could predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest and if predictions varied for control and salt-treated plants. A Random Forest approach was used to model biomass and yield. The results showed that shape features such as plant area, border length, width and length had the highest importance in the random forest models. A week prior to harvest, the explained variance of fresh shoot mass, number of fruits and yield mass were 86.60%, 59.46% and 61.09%, respectively. The explained variance was reduced as a function of time to harvest. Separate models may be required for predicting yield of salt-stressed plants, whereas the prediction of yield for control plants was less affected if the model included salt-stressed plants. This research demonstrates that it is possible to predict biomass and yield of tomato plants up to four weeks prior to harvest, and potentially earlier in the absence of severe weather events.
Highlights
Biomass and yield are two important variables used for informed decision-making and management of agricultural production
The aim of this research was to assess the ability of Unmanned Aerial Vehicle (UAV)-based imagery, collected one, two, four, six, seven and eight weeks before harvest to predict fresh shoot mass, yield mass and and tomato fruit numbers at harvest and determine how predictions varied for control and salt-treated plants
Biomass and yield was predicted for each of the delineated tomato plants based on the extracted variables using the Random Forest algorithm
Summary
Biomass and yield are two important variables used for informed decision-making and management of agricultural production. Measurements of biomass provide information on a plant’s ability to capture sunlight, water and minerals, and turn these into plant material and help determining amounts of fertilizer and irrigation of crops to be applied. Accurate yield forecasting during the growing season provides useful information for growers, allowing application of variable rates of inputs (water, fertilizer, pesticides) and logistical planning of field operations, including harvest scheduling and determining requirements for fruit picking, storage, packaging, and transportation and sales of fruit to wholesalers (Robson et al, 2017). The aim of this research was to assess the ability of UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest to predict fresh shoot mass, yield mass and and tomato fruit numbers at harvest and determine how predictions varied for control and salt-treated plants
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