## 1 Introduction  
The fourth industrial revolution, so-called Industry 4.0, has changed several sectors of the economy. The data generated in every process, which used to be a differentiator, is now necessary to maintain competitiveness in the market [1]. Besides the industrial scenario, agribusiness can also take advantage of the new technologies. Genetic engineering of plants, nanotechnology, biometric sensing, electrical agricultural machinery, computer vision integrating robotics with artificial intelligence (AI), and blockchain are examples of emerging technologies that have the potential to address challenges in all field states of the agricultural chain, pre-field, in-field, and post-field [2]. Agriculture 4.0 is based on principles such as data-driven decision-making, connectivity of devices, and growth of productivity, with added goals of adaptation to climate change and reduction of food waste [3; 4; 5]. Digital transformation can help farmers forecast the weather, control plaques, control temperature, and control moisture as well as uncountable other applications [6; 7]. Capacity planning was approached by the examination of capacity investment decisions employing a multiperiod model to study the optimal one-time processing and storage capacity investment decisions [8] or for lot-sizing and pricing when supply and price-sensitive demand are uncertain [9]. Moreover, ref. [10] developed a capacity planning model for transport to estimate the number of locomotives and shifts, the number of bins, and the delays to harvesting operations resulting from harvesters waiting for bin deliveries. Nevertheless, machine learning algorithms could excel when facing decision-makingproblems based on historical data [11]. According to [12], \"machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks\". It can forecast, cluster, and classify data. Thus, it can generate knowledge that helps decision-makers with problems such as how many machines to buy, how many employers to hire, or how many products to manufacture in the next month [13]. Machine learning methods have been successfully applied to solve several problems in production and logistics, such as forecasting customer demands [14], predicting energy consumption [15; 16], or making travel time predictions [17]. In agribusiness, machine learning technique applications include crop yield production [18], predicting soil properties [19], irrigation management [20], weather prediction [21], crop quality [22], harvesting [23], demand prediction [24], detecting vegetable diseases [25], and determining crop production [26]. For transportation, machine learning algorithms were applied for the vehicle routing problem [27; 28; 29] and to model the potential distribution of different plant species [30]. Nevertheless, simulation was applied to agribusiness to understand factors influencing the long-term viability of an intermediated regional food supply network [31]; to analyze firms' choices of spatial pricing policy [32]; to explore a hypothesis regarding the adoption of capabilities for entrepreneury [33]; to model human behavior in food supply chains with asymmetric information about food quality and food safety [34]; to understand how technology, market dynamics, environmental change, and policy intervention affect a heterogeneous population of farm households and resources [35]; to analyze how adaptation affects the distribution of household food security and poverty under current climate and price variability [36]; to understand how government payments to enhance public values in social-ecological systems can contribute to the resilience of the system [37]; to explore nutrient mitigation potentials of different policy instruments [38].