In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection.

A key point to decrease water usage in agriculture is to employ precision agriculture by adopting technologies such as the Internet of Things (IoT). Many solutions using the IoT rely on the use of soil moisture sensors to gather data, in real-time, from the plant’s root zone. This study presents a linear calibration curve, obtained by the thermogravimetric method, for a capacitive soil moisture sensor for a silty clay soil sampled at the FEI University Center. The curve obtained has an adjusted R ² of 0.86 and a residual standard error of 2.8%. The soil sensor was tested in the field during the end of February revealing that the sensor is capable of indicating changes in soil moisture within the error obtained from the calibration procedure and that it can be used to indicate changes by rain or irrigation procedures.

Applying the IoT paradigm in agriculture generates an unprecedented amount of data about fields and crops worldwide. With data, machine learning is natural to forecast or estimate crop parameters, which is remarkable for the estimation of evapotranspiration and soil moisture to propose irrigation plans and control, among other applications. This work investigates the literature to evaluate what kind of IoT devices and machine learning algorithms are being applied in precision agriculture and discusses the effectiveness of such equipment and algorithms on different spatial scales. The results indicate IoT devices are commonly applied in small crop areas or greenhouses and for short periods of time, usually less than one growing season. Machine learning models are also applied in similar scenarios, and the effectiveness of such techniques is discussed according to growers' needs and ambitions.

Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies.

Irrigation automation and Internet of Things (IoT) smart sensing improve water management and reduce labor in broadacre farming. This work investigates the feasibility of an innovative Wireless Fidelity (Wi-Fi) wireless mesh sensor network (WMSN) architecture to provide large-scale coverage (up to ~900 ha) and assesses its performance against the influence of crop canopy development. The study, conducted on two commercial rice farms, demonstrated the WMSN’s effectiveness in providing stable coverage with 98.89% message success after one retransmission. The network maintained a signal strength (RSSI) above the −85 dBm operational threshold within 800 m of a communication pod, with a stable mesh link established between pods up to 853 m apart. Results showed a strong negative linear correlation between rice canopy height and RSSI, with attenuation indices ranging from 2.34 to 2.88. Positioning the equipment 1.5 m above the canopy effectively mitigated the attenuation, achieving near free-space conditions.