Spatial Sensing Of Fruit Colour And Maturity In Stone Fruit Orchards
Creators: name: Islam, Maidul
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 400104
subject: Avionics
classificationCode: 400107
subject: Satellite, space vehicle and missile design and testing
classificationCode: 400103
subject: Aircraft performance and flight control systems

description: The estimation of pre-harvest fruit maturity, quality, and ripening processes are essential for growers to determine the harvest timing, storage, and profitability of the crop yield. Traditionally, growers harvest fruits and crops by manual labour that is highly laborious and contributes to waste when immature or over-ripe fruits are harvested. In addition, the parameters of in-field fruit maturity are highly variable and require high-resolution monitoring and measurement. Hence, measurements of multiple observables altogether can ensure fruit maturity more accurately rather than a single observable. Due to the recent advancement of remote sensing (RS) technologies, it is widely applied in Precision Agriculture (PA) for crop/fruit monitoring, diagnosis, harvesting and improving productivity. Unmanned Aerial System (UAS) with onboard sensors is a promising platform for quick data acquisition that further allows performing analysis of the data to understand the physiological or organoleptic changes in the crops/fruits. This work, funded by Food Agility CRC, proposes an RS system where a UAS is equipped with a passive electro-optic sensor (such as multispectral camera), assisted by an active electro-optic (such as bistatic Light Detection and Ranging (LIDAR)) to estimate the fruit maturity level and identify mature fruits in situ. In order to interpret the sensor data from the perspective of a fruit's physiological changes, an initial organoleptic study on stone-fruits i.e., peaches (Prunus persica) and nectarines (Prunus persica var. nucipersica) have been considered. As part of this research activity, observables like Index of Absorbance Difference (IAD), Flesh Firmness (FF) and Soluble Solid Concentration (SSC) are observed over the weeks to characterise their progress. Establishing a relationship between two observables allows estimating an observable when one is unmeasurable due to limited onboard sensors. Thus, this work also contributes to identifying the relationship between IADs and FFs that show strong reliability in terms of predicting each other as evidenced by an R² value of 0.62, an adjusted R² of 0.61, and a root mean square error (RMSE) of 1.25. Lab and in-field tests extrapolate strong correlations between observables (i.e., IADs and FFs) and vegetation indices (VIs), i.e., Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge (NDRE), Ratio of Red and NIR (R/NIR) and Two-band Enhanced Vegetation Index (EVI2). These VIs are measured from spectral images of five different channels such as red, green, blue, red-edge and NIR. Statistical study on VIs shows GNDVI has been identified as comparatively more reliable in explaining IADs and FFs and classifying peaches and nectarines into mature and immature categories. The GNDVI exhibits correlations with IAD and FF, characterized by R² values of 0.20 and 0.95, adjusted R² values of 0.95 and 0.95, and RMSE values of 0.12 and 0.08, respectively, for nectarines. In contrast, for peaches, the corresponding relationships are quantified with R² values of 0.94 and 0.64, adjusted R² values of 0.94 and 0.59, and RMSE values of 0.10 and 0.27. Besides, bistatic LIDAR contributes to determining a suitable time to harvest the fruits by measuring the status of the CO2 concentration of the orchard. Notably, Machine Learning (ML) classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbour (KNN), and Neural Network (NN), have been utilized with observables and VIs to classify the fruits several times with different datasets. The results show that SVM is the most reliable among them since it assures a comparatively lower misclassification rate (below 10%) irrespective of dataset sizes and variances and allows principal component analysis (PCA) to detect the most dominant features during the training. Successful outcomes from lab tests and in-field tests lead the project for the field trip to Tatura SmartFarm. Following the acquisition of data from the onboard spectral sensor, post-processing analysis confirmed the consistency of findings with lab and field test results. Specifically, the analysis validated the reliability of Support Vector Machines (SVMs) as an effective machine learning classifier, achieving a testing accuracy of 96.3%, and as a robust estimator, with RMSE of 0.42 and R² of 0.45 in testing. Furthermore, the GNDVI demonstrated superior performance compared to other vegetation indices, accounting for 75% of the variance within a dataset of 82 samples. Thus, this research work makes a major contribution to the field of PA by introducing a novel approach of utilizing an onboard multispectral camera and ML approaches including classifiers and regressors, to classify fruits and estimate fruit quality in situ in an orchard. In this work, the bistatic LIDAR plays the role of determining the ideal harvest time which is still challenging for a grower to determine correctly since weather and season contribute significantly to its changing. Finally, Key Performance Indicators (KPIs) based evaluation delineates the scopes and limitations of the proposed RS system’s efficacy and employability.