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As the global population increases, there is pressure to decrease the cost and reliance on human labour of farming. Deep learning image analysis for agricultural applications is a growing area of research, however most studies in the field of fruit detection have only focused on ripe fruit for mechanical harvesting. Commercial apple orchards require the thinning of fruitlets to maintain the quality of the crop, which is largely done by hand and is the second largest expense in commercial apple production. This paper proposes a deep learning method of assessing the density of fruitlets to enable chemical thinning, which requires the detection of fruit at an earlier developmental stage than has been previously researched. The novel proposed pipeline could be used to apply more accurate volumes of chemical thinner or to apply variable volumes based on the requirements of individual clusters that would significantly reduce the costs associated with hand-thinning. This study seeks to examine the suitability of four existing object detection architectures for detecting fruitlet clusters and four existing classification architectures for counting the number of fruitlets in each cluster. The detection networks achieved a maximum recall of 0.480 and the most successful classification network achieved at least 99% accuracy for each class.

Imaging beyond RGB bands has the ability to provide solutions for non-destructive detection of objects or scene properties. Methods often used in research include hyperspectral cameras and spectrometers; the former is expensive and the latter can only provide point measurements. Multispectral imaging can provide low-cost solutions in instances where RGB does not suffice, however, evaluation of filters used in multispectral sensors or the use of off-the-shelf filters for user specific applications have not been explicitly analysed. This paper proposes a novel method for using spectrometer data by firstly limiting the search space to existing off-the-shelf filters, modelling those filters, and then applying the desired model for classification or regression to more appropriately model multispectral imaging performance. The results indicate that point measurements produced by spectrometers may not correspond with the response achieved when using off-the-shelf filters; particularly in the instance where the spectral profile of the measured object varies significantly within the transmission regions of the filter. The method presented focuses on the practical estimation of filter performance when selecting filters.

© 2018 Australian Society of Viticulture and Oenology Inc. Background and Aims: Rapid and non-destructive techniques for measuring grape composition have the potential to improve harvest decisions and to allow the batching of fruit into parcels of similar composition to improve processing efficiency. If such techniques are to be based on the non-destructive assessment of visible berries, then relationships between the composition of visible berries and that of the whole bunch will need to be described. Methods and Results: Total soluble solids, pH, TA and the concentration of anthocyanin, phenolic substances and tannin of visible berries and associated entire bunches of Shiraz and Chardonnay were measured over three seasons. Good correlations were obtained between the composition of visible berries and that of the entire bunch. Most relationships were not 1:1 but were consistent and stable across seasons. Conclusions: The accuracy and stability across the seasons of relationships between the composition of visible berries and that of the entire bunch for both Chardonnay and Shiraz were evaluated. Significance of the Study: The approaches used in this study to predict bunch composition from visible berry composition could aid the development of non-destructive techniques based on sensing visible berries to assess grape composition during ripening.

© 2018 IAgrE Automated flower counting systems have recently been developed to process images of grapevine inflorescences, which assist in the critical tasks of determining potential yields early in the season and measurement of fruit-set ratios without arduous manual counting. In this paper, we introduce a robust flower estimation system comprised of an improved flower candidate detection algorithm, flower classification and finally flower estimation using calibration models. These elements of the system have been tested in eight aspects across 533 images with associated manual counts to determine the overall accuracy and how it is affected by experimental conditions. The proposed algorithm for flower candidate detection and classification is superior to all existing methods in terms of accuracy and robustness when compared with images where visible flowers are manually identified. For flower estimation, an accuracy of 84.3% against actual manual counts was achieved both in-vivo and ex-vivo and found to be robust across the 12 datasets used for validation. A single-variable linear model trained on 13 images outperformed other estimation models and had a suitable balance between accuracy and manual counting effort. Although accurate flower counting is dependent on the stage of inflorescence development, we found that once they reach approximately EL16 this dependency decreases and the same estimation model can be used within a range of about two EL stages. A global model can be developed across multiple cultivars if they have inflorescences with a similar size and structure.

A fast image processing method is proposed for detecting stomata and measuring stomatal aperture size in individual images. The accuracy of aperture measurements is 97%. A prototype mobile application is developed to assist field measurements.