Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia

Abstract

Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree −1 ) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg·tree −1 ) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg·tree −1 ) (R 2 = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R 2 = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R 2 values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg·tree −1 ) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates.

Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango

“…The name of VIs are NDVI red-edge, TCARI, SIPI, CB SIPI, R/RENDVI, R/N2NDVI, GNDVI, MSR, RVI, N1NDVI, N2NDVI, RENDVI1, RENDVI2, RDVI1, RDVI2, TDVI1, TDVI2 and NLI. The detailed of the 18 VIs can be found in previous work elsewhere [36]. The VI with highest coefficient of determination and lowest root mean squares error (RMSE) to the measured parameter was identified for each orchard.…”

“…Very high resolution satellite imagery offers as a time efficient and more convenient solution of mango yield parameter estimation on a large scale. Whilst prior literature has demonstrated the potential benefit of this technology for measuring yield in annual crops [23][24][25][26][27], the results for perennial fruit tree crops have revealed varying level of success, for example citrus [28,29], apples [30,31], pears [32], peach [33], olive [33], grapes [34,35], avocado [36,37], macadamia [37] among others. For mango, a limited number of satellite based remote sensing studies have been undertaken and their main objective was mostly limited to outlining the various mango orchards in a regional level [38,39].…”

“…Recently, with the advent of 30 cm resolution panchromatic WV3 imagery, it is now conceivable to calculate tree crown area using geographic object based image analysis (GEOBIA) and canopy reflectance measured across eight band multispectral imagery. Improving the accuracy of yield parameter estimation with lower sampling effort is another important issue in fruit crop industry, which could potentially be achieved by segregating the orchards into clusters of trees with similar crop vigour or normalized difference vegetation index (NDVI) from WV3 imagery [36]. The aim of this paper is to evaluate WV3 imagery for the accurate forecasting of mango yield via the integration of tree crown area (TCA) and canopy reflectance properties of individual trees.…”

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

“…As such, measurements of these parameters can provide growers with a strong indication of plant health or vigour, photosynthetic capacity and yield potential [13,14]. In most Australian horticultural industries, such assessment is usually conducted by on-ground visual evaluation, which is time-consuming, labour-intensive, subjective and often inconsistent [7,[15][16][17]. Therefore, there is a demand for more efficient, accurate and quantitative alternatives for such assessments.…”

Predicting Biomass and Yield at Harvest of Salt-Stressed Tomato Plants Using Uav Imagery

“…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). While effects of salinity will generally reduce biomass and yield of plants, it is not well understood how salinity affects the ability to predict biomass and yield.…”