Soybean (Glycine max L.) has become the most important oilseed crop and one of the five most important food crops worldwide [1,2]. Its high protein content makes soybean a prime source of feed for livestock, and soybean oil is used for both human consumption and industrial applications [1]. While the demand for soybeans continues to grows worldwide [3], environmental pressures due to climate change are becoming more widespread and extreme [4]. In order for the soybean yield to keep up with the demand, new solutions to current production limitations are needed. Although extensive breeding efforts have led to the development of varieties quite robust to different conditions, soybean crops are still vulnerable to many factors. Stresses caused by diseases, pests, unfavorable weather, nutrition imbalances, and others, are responsible for losses that can easily surpass 20% of the total world production [2]. Although completely eliminating losses is likely unfeasible, closely monitoring each one of the relevant variables can greatly mitigate the problem [5]. However, continuous monitoring may require too large a workforce, unless some type of automation is employed. In this context, artificial intelligence techniques emerge as powerful aiding tools for farm monitoring and management [6].

One of the possible definitions for artificial intelligence (AI) states that this is “a computational data-driven approach capable of performing tasks that normally require human intelligence to independently detect, track, or classify objects” [7]. Techniques fitting this definition have existed for many decades, including expert systems, neural networks and other types of machine learning algorithms. With the inception of deep learning models in the first half of the 2010s, the application of artificial intelligence has grown steeply both in number and scope. This is certainly true in the case of agriculture, for which applications like plant disease recognition [5], yield estimation [8], plant nutrition status [9], and biomass estimation [10], among many others, have experienced a surge in the number of articles employing artificial intelligence. Among AI techniques, deep learning has been particularly successful and well adapted to difficult classification problems. One of the reasons for this success is that with deep learning, the explicit extraction of features from the data is no longer required [11,12], making the classification process more straightforward, less biased and more robust to different types of conditions [13].

While the leap between academic research and practical solutions has been successfully completed in some cases (e.g., weed detection and control), in most cases, real-world conditions and variability are too challenging for techniques and models that are, more often than not, trained on data that represent only a small fraction of reality [14]. The most direct way to address this problem is to expand the datasets used to train the models. This is by no means a trivial task, especially considering that the variability involved in some classification problems may require a number of images that can reach the order of millions. Increasing the practice of data sharing and exploring citizen science concepts can help reduce the problem, but in many situations, all-encompassing datasets may be unfeasible [14]. If supervised learning is adopted, there is the additional challenge of data annotation, a process that is often expensive, time-consuming and error-prone [15]. New annotation strategies capable of speeding up the process are already being studied [16], but these were still incipient when this article was written.

AI models failing when presented with new data with distinct statistical distribution, a phenomenon often called “covariate shift” [17], is arguably the most important hurdle for the more effective use of artificial intelligence-based technologies in agriculture, but other factors are almost always present. Each application has its own challenges, so systemically understanding how data and technical issues affect the performance of the models is fundamental for the construction of suitable solutions. Many of these challenges have already been experienced in previous studies and reported in the literature, so a proper understanding of the current state of the art is critical to the novelty of new research and to avoid repeating mistakes. Research on deep learning applied to crop management has been extensive across different types of crops, so including all research would make the article somewhat redundant and impractically long. Soybean, being a major agricultural commodity, has received considerable attention from researchers, to the point that it encapsulates most of the approaches adopted across different crops. This was the main motivation for narrowing the scope of the review to research related to this crop only.

The use of deep learning for soybean monitoring started to gain momentum after 2015. Early research was mostly dedicated to disease and pest detection, but soon, applications like phenotyping, seed counting, cultivar identification and yield prediction began being explored. Since the beginning, studies have been focusing on the investigation of different deep learning models and architectures in the context of each different application and domain. Although this type of research has yielded relevant results, there are not many technologies being effectively used in practice. One important exception is weed detection, as machinery from different manufacturers already have the ability to not only detect the weeds but also actuate to eliminate the problem. For most applications, there are still significant challenges that require more suitable solutions. New approaches emphasizing model interpretability and fine tuning are beginning to be explored [18,19,20], but the research gap is still substantial.

2. Definitions and Acronyms