Knowledge-based synergistic automation is a potential intermediate option between the opposite extremes of manual and fully automated robotic labor in agriculture. Disruptive information and communication technologies (ICT) and sophisticated solutions for human-robot interaction (HRI) endow a skilled farmer with enhanced capabilities to perform agricultural tasks more efficiently and productively. This research aspires to apply systems engineering principles to assess the design of a conceptual human-robot synergistic platform enabled by a sensor-driven ICT sub-system. In particular, this paper firstly presents an overview of a use case, including a human-robot synergistic platform comprising a drone, a mobile platform, and wearable equipment. The technology framework constitutes a paradigm of human-centric worker-robot logistics synergy for high-value crops, which is applicable in operational environments of outdoor in-field harvesting and handling operations. Except for the physical sub-system, the ICT sub-system of the robotic framework consists of an extended sensor network for enabling data acquisition to extract the context (e.g., worker’s status, environment awareness) and plan and schedule the robotic agents of the framework. Secondly, this research explicitly presents the underpinning Design Structure Matrix (DSM) that systematically captures the interrelations between the sensors in the platform and data/information signals for enabling synergistic operations. The employed Systems Engineering approach provides a comprehensible analysis of the baseline structure existing in the examined human–robot synergy platform. In particular, the applied DSM allows for understanding and synthesizing a sensor sub-system’s architecture and enriching its efficacy by informing targeted interventions and reconfiguring the developed robotic solution modules depending on the required farming tasks at an orchard. Human-centric solutions for the agrarian sector demand careful study of the features that the particular agri-field possesses; thus, the insight DSM provides to system designers can turn out to be useful in the investigation of other similar data-driven applications. Full article

Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0. Full article

The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time. Full article

Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop growth. However, this rapid growth process exacerbates the tip-burn problem, especially for lettuce. This paper presents an automated detection of tip-burn lettuce grown indoors using a deep-learning algorithm based on a one-stage object detector. The tip-burn lettuce images were captured under various light and indoor background conditions (under white, red, and blue LEDs). After augmentation, a total of 2333 images were generated and used for training using three different one-stage detectors, namely, CenterNet, YOLOv4, and YOLOv5. In the training dataset, all the models exhibited a mean average precision (mAP) greater than 80% except for YOLOv4. The most accurate model for detecting tip-burns was YOLOv5, which had the highest mAP of 82.8%. The performance of the trained models was also evaluated on the images taken under different indoor farm light settings, including white, red, and blue LEDs. Again, YOLOv5 was significantly better than CenterNet and YOLOv4. Therefore, detecting tip-burn on lettuce grown in indoor farms under different lighting conditions can be recognized by using deep-learning algorithms with a reliable overall accuracy. Early detection of tip-burn can help growers readjust the lighting and controlled environment parameters to increase the freshness of lettuce grown in plant factories. Full article

Secondary infections of early blight during potato crop season are conditioned by aerial inoculum. However, although aerobiological studies have focused on understanding the key factors that influence the spore concentration in the air, less work has been carried out to predict when critical concentrations of conidia occur. Therefore, the goals of this study were to understand the key weather variables that affect the hourly and daily conidia dispersal of Alternaria solani and A. alternata in a potato field, and to use these weather factors in different machine learning (ML) algorithms to predict the daily conidia levels. This study showed that conidia per hour in a day is influenced by the weather conditions that characterize the hour, but not the hour of the day. Specifically, the relative humidity and solar radiation were the most relevant weather parameters influencing the conidia concentration in the air and both in a linear model explained 98% of the variation of this concentration per hour. Moreover, the dew point temperature three days before was the weather variable with the strongest effect on conidia per day. An improved prediction of Alternaria conidia level was achieved via ML algorithms when the conidia of previous days is considered in the analysis. Among the ML algorithms applied, the CART model with an accuracy of 86% were the best to predict daily conidia level. Full article

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network’s resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works. Full article

Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R²p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R²p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R²p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R²p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications. Full article

In contrast with classic bench-top hyperspectral (multispectral)-sensor-based instruments (spectrophotometers), the portable ones are rugged, relatively inexpensive, and simple to use; therefore, they are suitable for field implementation to more closely examine various soil properties on the spot. The purpose of this study was to evaluate two portable spectrophotometers to predict key soil properties such as texture and soil organic carbon (SOC) in 282 soil samples collected from proportional fields in four Canadian provinces. Of the two instruments, one was the first of its kind (prototype) and was a mid-infrared (mid-IR) spectrophotometer operating between ~5500 and ~11,000 nm. The other instrument was a readily available dual-type spectrophotometer having a spectral range in both visible (vis) and near-infrared (NIR) regions with wavelengths ranging between ~400 and ~2220 nm. A large number of soil samples (n = 282) were used to represent a wide variety of soil textures, from clay loam to sandy soils, with a considerable range of SOC. These samples were subjected to routine laboratory soil analysis before both spectrophotometers were used to collect diffuse reflectance spectroscopy (DRS) measurements. After data collection, the mid-IR and vis-NIR spectra were randomly divided into calibration (70%) and validation (30%) sets. Partial least squares regression (PLSR) was used with leave one out cross-validation techniques to derive the spectral calibrations to predict SOC, sand, and clay content. The performances of the calibration models were reevaluated on the validation set. It was found that sand content can be predicted more accurately using the portable mid-IR spectrophotometer and clay content is better predicted using the readily available dual-type vis-NIR spectrophotometer. The coefficients of determination (R²) and root mean squared error (RMSE) were determined to be most favorable for clay (0.82 and 78 g kg⁻¹) and sand (0.82 and 103 g kg⁻¹), respectively. The ability to predict SOC content precisely was not particularly good for the dataset of soils used in this study with an R² and RMSE of 0.54 and 4.1 g kg⁻¹. The tested method demonstrated that both portable mid-IR and vis-NIR spectrophotometers were comparable in predicting soil texture on a large soil dataset collected from agricultural fields in four Canadian provinces. Full article

The Global Positioning System (GPS) and sensors technologies are increasingly used to study the grazing behavior of animals. This work was conducted to understand the grazing behavior and energy balance of goats browsing in forest rangeland using GPS and sensors technologies. Forage availability was estimated using the quadrat method during three grazing seasons. Simultaneously, eight indigenous goats were selected to explore their feeding behavior, grazing activities, and energy requirements. The experimental goats were fitted with GPS collars and leg sensors to monitor their grazing activities. At the same time, direct observation was used as a method to study their feeding behavior. Forage availability was higher during spring compared to the summer and autumn seasons. Goats recorded the highest biting rate during summer and autumn (about 22 bites/min). The highest intake rate was recorded during spring (5.6 g DM/min). During spring, goats spent most of their time on grazing (48%) in contrast to the summer and autumn (<31%; p < 0.001). They prolonged their lying down time in summer at the expense of standing duration. The time devoted exclusively to grazing (eating) was longer in spring. Walking time in summer and autumn was longer than in spring (p < 0.001). During summer and autumn, the energy balance of goats under grazing conditions was in deficit. Using GPS collars and leg sensors appears to be a useful and easily replicable method to explore and understand the seasonal changes in the grazing areas and activities of goats in a mountainous region. The results could help goat herders and managers to develop feeding and grazing systems while increasing the performance of goats in the Mediterranean forest rangeland. Full article

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha). Full article

In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE–PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field. Full article

This study was conducted to analyze the effects of tillage depth and gear selection on the mechanical load and fuel efficiency of an agricultural tractor during plow tillage. In order to analyze these effects, we developed an agricultural field measuring system consisting of a load measurement part (wheel torque meter, proximity sensor, and real-time kinematic (RTK) global positioning system (GPS)) and a tillage depth measurement part (linear potentiometer and inclinometer). Field tests were carried out using moldboard plows with a maximum tillage depth of 20 cm and three gear selections (M2H, M3L, and M3H) in a rice stubble paddy field for plow tillage. The average travel speed and slip ratio had the lowest M2H and the highest M3L. M3H had the highest theoretical speed, but the travel speed was 0.13 km/h lower than M3L due to the reduction in the axle rotational speed at deep tillage depth. Regarding engine load, the higher the gear, the greater the torque and the lower the axle rotation speed. The front axle load was not significantly affected by the tillage depth as compared to other mechanical parts, except for the M3H gear. The rear axle load generated about twice the torque of the front wheel and overall, it tended to show a higher average rear axle torque at higher gear selection. The rear axle load and fuel rate were found to be most affected by the combination of the tillage depth and gear selection combination. Overall, field test results show that the M3H had the highest fuel efficiency and a high working speed while overcoming high loads at the same tillage depth. In conclusion, M3H is the most suitable gear stage for plow cultivation, and the higher the gear stage and the deeper the tillage depth during plowing, the higher the fuel efficiency. The results of this study will be useful for analyzing mechanical load and fuel efficiency during farm operations. In a future study, we will conduct load analysis studies in other farming operations that consider various soil mechanics factors as well as tillage depths and gear selections. Full article

The growing global demand for food and the environmental impact caused by agriculture have made this activity increasingly dependent on electronics, information technology, and telecommunications technologies. In Colombia, agriculture is of great importance not only as a commercial activity, but also as a source of food and employment. However, the concept of smart agriculture has not been widely applied in this country, resulting in the high production of various types of crops due to the planting of large areas of land, rather than optimization of the processes involved in the activity. Due to its technical characteristics and the radio spectrum considered in its deployment, 5G can be seen as one of the technologies that could generate the greatest benefits for the Colombian agricultural sector, especially in the most remote rural areas, which currently lack mobile network coverage. This article provides an overview of the current 5G technology landscape in Colombia and presents examples of possible 5G/IoT applications that could be developed in Colombian fields. The results show that 5G could facilitate the implementation of the smart farm in Colombia, improving current production and efficiency. It is useful when designing 5G implementation plans and strategies, since it categorizes crops by regions and products. This is based on budget availability, population density, and regional development plans, among others. Full article

In our project herein, we use the case of farmworkers, an underserved and understudied population at high risk for Type-2 Diabetes Mellitus (T2DM), as a paradigm of an integrated action-oriented research, education and extension approach involving the development of long-term equitable strategies providing empowerment and tailored-made solutions that support practical decision-making aiming to reduce risk of T2DM and ensuing cardiovascular disease (CVD). A Technology-based Empowerment Didactic module (TEDm) and an Informed Decision-Making enhancer (IDMe) coupled in a smart application (app) for farmworkers aiming to teach, set goals, monitor, and support in terms of nutrition, hydration, physical activity, sleep, and circadian rhythm towards lowering T2DM risk, is to be developed and implemented considering the particular characteristics of the population and setting. In parallel, anthropometric, biochemical, and clinical assessments will be utilized to monitor risk parameters for T2DM and compliance to dietary and wellness plans. The app incorporating anthropometric/clinical/biochemical parameters, dietary/lifestyle behavior, and extent of goal achievement can be continuously refined and improved through machine learning and re-programming. The app can function as a programmable tool constantly learning, adapting, and tailoring its services to user needs helping optimization of practical informed decision-making towards mitigating disease symptoms and associated risk factors. This work can benefit apart from the direct beneficiaries being farmworkers, the stakeholders who will be gaining a healthier, more vibrant workforce, and in turn the local communities. Full article