A Case Study of a Decision Support System on Mango Fruit Maturity

Mango fruit maturity can be difficult to determine from external attributes. Assessment of parameters of fruit on tree (dry matter, internal flesh colour) relevant to estimation of fruit maturity was undertaken with a handheld (near infrared spectroscopic) system. Measurement error on dry matter was low (typical RMSEP 0.6% DM). Repeated measurements on the same individual fruit from 78 different blocks across two farms demonstrated that each piece of fruit was on a similar, but individual, maturation trajectory, with a time offset. The offset was presumably related to date of pollination or environmental conditions around the fruit (e.g., inner or outer canopy). A non-linear indexed regression model, coupled with the use of a ‘biological shift factor’, was used to describe the time series data. Estimated biological shift factors were larger for dry matter than flesh colour, indicative of an earlier change in dry matter, albeit at a lower rate. Differences between blocks within a farm and between two farms were small, indicating the maturation processes were independent of local conditions. This technique could be used to trace the source of variation within a block (e.g., to location in canopy or plant water status), towards the goal of reducing this variation, leading to crops of greater uniformity.