Quantum computers use the properties of quantum physics to perform information storage and processing operations. The operation of these computers involves concepts such as entanglement and superposition, which endow them with a great processing power that even surpasses that of the most powerful current supercomputers, while consuming significantly lower amounts of energy. The different studies analyzed in this review article suggest that quantum computing will have a deep impact in areas such as finance, logistics, transportation, space and automotive technology, materials science, energy, pharmaceutical and healthcare industry, cybersecurity, and agriculture. In digital agriculture, several applications that could be executed more efficiently in quantum computers for data processing and understanding of biological processes were identified and exemplified. These applications are grouped here into the following four areas: bioinformatics, remote sensing, climate modeling, and smart farming. This article also explores the strategic importance of mastering quantum computing, highlights some advantages in relation to classical computing, and presents a mapping of the services already available, enabling institutions to undertake strategic planning for the incorporation of quantum computing into their development processes. Finally, the challenges for the implementation of quantum computing are highlighted, along with some ongoing initiatives aimed at furthering research at the forefront of knowledge in this area applied to digital agriculture.

Traditionally, sweet orange crop forecasting has involved manually counting fruits from numerous trees, which is a labor-intensive process. Automatic systems for fruit counting, based on proximal imaging, computer vision, and machine learning, have been considered a promising alternative or complement to manual counting. These systems require data association components that prevent multiple counting of the same fruit observed in different images. However, there is a lack of work evaluating the accuracy of multiple fruit counting, especially considering (i) occluded and re-entering green fruits on leafy trees, and (ii) counting ground-truth data measured in the crop field. We propose a non-invasive alternative that utilizes fruit counting from videos, implemented as a pipeline. Firstly, we employ CNNs for the detection of visible fruits. Inter-frame association techniques are then applied to track the fruits across frames. To handle occluded and re-appeared fruit, we introduce a relocalization component that employs 3-D estimation of fruit locations. Finally, a neural network regressor is utilized to estimate the total number of fruit, integrating image-based fruit counting with other tree data such as crop variety and tree size. The results demonstrate that the performance of our approach is closely tied to the quality of the field-collected videos. By ensuring that at least 30% of the fruit is accurately detected, tracked, and counted, our yield regressor achieves an impressive coefficient of determination of 0.85. To the best of our knowledge, this study represents one of the few endeavors in fruit estimation that incorporates manual fruit counting as a reference point for evaluation. We also introduce annotated datasets for multiple orange tracking (MOrangeT) and detection (OranDet), publicly available to foster the development of novel methods for image-based fruit counting.