A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

Abstract

Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes a distribution framework consisting of the acquisition of fruit tree images, yield prediction in smartphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). An individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups of data were validated. The R 2 value for the training datasets was 0.996 and the relative root meansquared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284.Furthermore, a yield map with 80 apple trees was generated, and the space distribution of the yield was identified. It provided appreciable decision support for site-specific management.

Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning

“…The current work was inspired by reports of direct estimation of total fruit load per tree from tree images [10][11][12]. The use of machine learning models trained directly on fruit number per tree rather than fruit number per image avoids the need for manual estimation of an occlusion factor every season.…”

“…For the Pinova variety, a 4-10-1 architecture model achieved R 2 of 0.88 and RMSE of 2.5 kg/tree in estimation of a test set. In a parallel approach, [12] trained an ANN model (4-14-1 architecture) with four image features (total fruit pixel area, circle fitted fruit pixel area, average radius of fitted circle and residual fruit pixel area after circle fitting) from dual view images of apple trees to predict individual tree yield (kg/tree). The images presented were of very narrow canopies with very low rates of occluded fruit.…”

Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

“…The creation of models using DIP with digital cameras is a low-cost method [19]. Herein, Android mobile technology estimated the number of days and the state of ripeness of the Hass avocados in their post-harvest phase.…”

“…Colors are extensively used to assess fruit quality and ripeness owing to their relation with physical and chemical changes in fruits [18]. Furthermore, different agricultural techniques have been developed using digital image processing [19,20].…”

3DBunch: A Novel iOS-Smartphone Application to Evaluate the Number of Grape Berries per Bunch Using Image Analysis Techniques

“…Gong et al [13] used an Android mobile phone to estimate the yield of citrus on an individual tree by image processing, and Cubero et al [14] developed an application to facilitate the measurement of citrus colour index. In order to estimate apple yield in a fast, convenient and low cost way, Qian et al [15] proposed a distribution framework based on smartphone and server client for image acquisition, yield prediction, data processing and model calculation and generate a yield map to identify the space distribution of the yield. Smartphone applications can also be applied to assess rice nutritional status [16] and detect damage caused by blue mould on tobacco leaves [17].…”