Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review

Abstract

Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

Honey-Guacamole: Assessment of pollination environmental service in avocado production in Michoacan, Mexico

“…Despite that a linear production function was used, the avocado yield estimates obtained are in the same range of goodness of fit than those reported estimates from other authors, as they explain a bit more than 75% of the observed variance (Li et al, 2018;Lobell et al, 2007;Rincon-Patino et al, 2018). In the same order of ideas, results show that the most influential variables to predict avocado yield in Michoacan coincide with most yield reports, which indicate that climatic variables are the most determinant to predict avocado yield (Rincon-Patino et al, 2018).…”

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

“…The outcomes for the coefficient between 0.9056 to 0.9834 for the ANN model confirmed that it is a precise tool with very good prediction accuracy. Moreover, ANN engaged in the evaluation of fruit ripeness and the prediction of a harvest date confirm that the ANN modelling has high accuracy and can be used as a predictive tool for perishable and prolonged shelf-life foodstuffs [35].…”

Revestimento biodegradável à base de Spirulina platensis na conservação pós-colheita de goiaba Paluma mantidas sob diferentes temperaturas de armazenamento

“…Diante desse cenário, há a necessidade da adoção de técnicas que mantenham e prolonguem a vida pós-colheita dos frutos. Existem diferentes técnicas para a manutenção da qualidade de frutas e hortaliças, dentre as quais destaca-se a aplicação de revestimentos poliméricos, refrigeração, atmosfera modificada e controlada, irradiação (Almeida, 2010;Li et al, 2018).…”