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Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.

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Apple and pear crops are very important to the rural economy of Portugal. Despite significant improvements in productivity and quality, due to the introduction of new management techniques, model-based decision support may further increase the revenue of the growers. Available simulation models of orchard growth and production are scarce and are often highly empirical. This study presents a mechanistic model for the simulation of productivity and fruit grade of apple and pear orchards under potential and water-limited conditions. The effects of temperature extremes and rain on fruit set are addressed. The model was validated on apple and pear datasets derived from extensive experiments conducted in Central and Southern Portugal. Model performance is high and depicts the effect of crop load on productivity and fruit-size grade and the distribution of both crops. A simulation example shows the relationship between productivity and average fruit size for a hypothetical six-year-olc apple orchard. The model herewith presented is a tool that can be used to estimate optimal crop load for maximum revenue and productivity, fruit size distribution, water use, and other variables relevant for pome fruit production.

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