AgriEngineering

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies. Full article

This study aimed to improve the seed quality during the deterioration period of rough rice (Oryza sativa L.), cultivar ‘Khoa Dawk Mali 105’ (KDML 105), using an automatic soaking and germination accelerator unit (ASGA) together with stimulation via infrared radiation treatment (IRT) to stimulate seed quality (germination rate and γ-aminobutyric acid (GABA) content). This study used a general full factorial design, and the independent variables were the storage period (10, 11 and 12 months), methods of germinated rough rice preparation (conventional method (CM) and an automatic soaking and germination accelerator unit (ASGA)), and stimulation with IRT. The initial grain moisture content did not exceed 14% (wet basis (wb)). The germination rate of the rough rice by CM and ASGA with stimulation with IRT was significantly higher than non-stimulated rice, by 6.56 and 8.11%, respectively, in each storage period. The GABA contents of the germinated rough rice using CM and ASGA stimulated with IRT were significantly higher than ungerminated rough rice, by 19.52 and 21.24% (10 months), respectively; 16.36 and 23.58% (11 months), respectively; and 69.88 and 67.69% (12 months), respectively. Full article

Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or in pest/disease detection. As sensing technology advancement expands, measuring nutrient levels and disease detection also progresses. This study aimed to predict three different levels of potassium (K) concentration in peach leaves using principal component analysis (PCA) and develop models for predicting the K concentration of a peach leaf using a hyperspectral imaging technique. Hyperspectral images were acquired from a randomly selected fresh peach leaf from multiple trees over the spectral region between 500 and 900 nm. Leaves were collected from trees with varying potassium levels of high (2.7~3.2%), medium (2.0~2.6%), and low (1.3~1.9%). Four pretreatment methods (multiplicative scatter effect (MSC), Savitzky–Golay first derivative, Savitzky–Golay second derivative, and standard normal variate (SNV)) were applied to the raw data and partial least square (PLS) was used to develop a model for each of the pretreatments. The R² values for each pretreatment method were 0.8099, 0.6723, 0.5586, and 0.8446, respectively. The SNV prediction model has the highest accuracy and was used to predict the K nutrient using the validation data. The result showed a slightly lower R² = 0.8101 compared with the training. This study showed that HSI could measure K concentration in peach tree cultivars. Full article

One of the most important problems during the implementation of any technology is to reduce labor costs, energy, and resource conservation while increasing the yield of cultivated crops and, as a result, reducing the cost of production. Despite a significant amount of scientific research devoted to the problem of energy and resource conservation in the cultivation and harvesting of agricultural crops and the development of mechanization tools that ensure the high-quality performance of technological operations, there remain issues that have not been fully resolved to date. In addition, not all the results of known theoretical and experimental studies can be directly applied to intensify the process of harvesting root crops since the quality indicators of marketable products depend on the type and technological parameters of the separating working bodies. This article presents the design of a rod elevator with an adjustable angle of inclination of the web, which reduces damage to commercial products of root crops and bulbs with maximum completeness of separation. A laboratory facility has been developed to substantiate the design and technological parameters of a separating system with an adjustable web inclination angle. Based on the results of theoretical and experimental studies, a machine for harvesting onions with an adjustable blade inclination angle has been developed, which provides an increase in the quality indicators of onion harvesting at optimal values of the parameters: (1) translational speed of movement of the rod elevator with an adjustable web inclination angle of 1.7 m/s with a 98.4% completeness of separation and 1.7% damage to the bulbs; (2) translational speed of the movement of the machine for harvesting root crops and onions 1.0 m/s with a 98.5% separation completeness and 1.1% damage to the bulbs; (3) digging depth of the digging plowshare equal to 0.02 m, with an onion heap separation completeness of more than 98% and product damage of less than 1.4%. The results of theoretical and experimental studies of a rod elevator to substantiate the design and technological parameters during its interaction with a heap of onion are presented. Basic design and technological parameters of the studied rod elevator are substantiated, namely, the distance S1 of the movement of the rod of the actuators, the angle a1 of the longitudinal inclination of the surface of the rod elevator relative to the horizon, and differential equations of motion of the onion-sowing pile element on the surface of the rod elevator with an adjustable angle of inclination of the web. Full article

Bird welfare and comfort is highly impacted by extreme environments, including hot/cold temperatures, relative humidity, and heat production within the coops during loading at the farm, transportation, and holding at the processing plants. Due to the complexity of the multiphysics phenomena involving fluid flow, heat transfer, and multispecies mixtures (humidity) within the coops, machine learning models may be helpful to evaluate broiler welfare under various environments. Machine learning techniques (Artificial Neural Networks and Bayesian Optimization) were applied to estimate the desired parameters required to ensure broiler welfare inside the coops. Artificial Neural Networks (ANNs) were trained with the results of Computational Fluid Dynamics (CFD) simulations for various ranges of inputs related to the microenvironment. Input variables included air velocity, broiler heat production, ambient temperature, and relative humidity. The Output variable was the Enthalpy Comfort Index (ECI), which is a measure of the bird welfare. The trained networks were then analyzed using Bayesian Optimization (BO) for the inverse mapping of ANNs and to predict the range of acceptable input parameters for a desired output, i.e., ECI in the comfort level. Results indicate that reducing the broilers heat production inside the coop along with increasing fan velocity enhances the broiler welfare and the thermal microenvironment. The BO developed in this study provide the microenvironmental parameters to estimate the bird welfare that is comfortable. Full article

In recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, and reduce labor costs. To achieve harvesting time prediction, various works are being actively conducted. Methods for harvesting time prediction using meteorological information such as temperature and solar radiation, etc., and methods for harvesting time prediction using neural networks based on color information from fruit bunch images are being investigated. However, the prediction accuracy is still insufficient, and the harvesting time prediction for individual tomato fruits has not been studied. In this study, we propose a novel method to predict the harvesting time for individual tomato fruits. The method uses Mask R-CNN to detect tomato bunches and uses two types of ripeness determination to predict the harvesting time of individual tomato fruits. The experimental results showed that the accuracy of the prediction using the ratio of R values was better for the harvesting time prediction of tomatoes that are close to the harvesting time, and the accuracy of the prediction using the average of the differences between R and G in RGB values was better for the harvesting time prediction of tomatoes that are far from the harvesting time. These results show the effectiveness of the proposed method. Full article

Mechanical perturbation constrains edaphic functionality of arable soils in tillage. Seasonal soil tool interactions disrupt the pristine bio-physio-mechanical characteristics of agricultural soils and crop-oriented ecological functions. They interfere with the natural balancing of nutrient cycles, soil carbon, and diverse organic matter that supports soil ecosystem interactions with crop rooting. We review soil working in tillage, associated mechanistic perturbations, and the edaphic response of affected soil properties towards cropping characteristics and behavior as soil working tools evolve. This is to further credit or discredit the global transition to minimum and no-till systems with a more specific characterization to soil properties and edaphic crop-oriented goals of soil tooling. Research has shown that improvement in adoption of conservation tillage is trying to characterize tilled soils with edaphic states of native soil agroecosystems rendering promising strategies to revive overworked soils under the changing climate. Soil can proliferate without disturbance whilst generation of new ecologically rich soil structures develops under more natural conditions. Researchers have argued that crops adapted to the altered physio-mechanical properties of cultivated soils can be developed and domesticated, especially under already impedance induced, mechanically risked, degraded soils. Interestingly edaphic response of soils under no-till soil working appeared less favorable in humid climates and more significant under arid regions. We recommend further studies to elucidate the association between soil health state, soil disturbance, cropping performance, and yield under evolving soil working tools, a perspective that will be useful in guiding the establishment of future soils for future crops. Full article

Optimum nitrogen (N) application is essential to the economic and environmental sustainability of cotton production. Variable-rate N fertigation could potentially help farmers optimize N applications, but current overhead irrigation systems normally lack automated site-specific variable-rate fertigation capabilities. The objective of this study was to develop an automated variable-rate N fertigation based on real-time Normalized Difference Vegetation Index (NDVI) measurements from crop sensors integrated with a lateral move irrigation system. For this purpose, NDVI crop sensors and a flow meter integrated with Arduino microcontrollers were constructed on a linear move fertigation system at the Edisto Research and Education Center in Blackville, South Carolina. A computer program was developed to automatically apply site-specific variable N rates based on real-time NDVI sensor data. The system’s ability to use the NDVI data to prescribe N rates, the flow meter to monitor the flow of N, and a rotary encoder to establish the lateral’s position were evaluated. Results from this study showed that the system could accurately use NDVI data to calculate N rates when compared to hand calculated N rates using a two-sample t-test (p > 0.05). Linear regression analysis showed a strong relationship between flow rates measured using the flow meter and hand calculations (R² = 0.95), as well as the measured distance travelled using the encoder and the actual distance travelled (R² = 0.99). This study concludes that N management decisions can be automated using NDVI data from on-the-go handheld GreenSeeker crop sensors. The developed system can provide an alternative N application solution for farmers and researchers. Full article

The coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. Therefore, this study aims to analyze vegetation indices (VI) from images of healthy coffee leaves and those infested by coffee leaf miner, obtained using a multispectral camera, mainly to differentiate and detect infested areas. The study was conducted in two distinct locations: At a farm, where the camera was coupled to a remotely piloted aircraft (RPA) flying at a 3 m altitude from the soil surface; and the second location, in a greenhouse, where the images were obtained manually at a 0.5 m altitude from the support of the plant vessels, in which only healthy plants were located. For the image processing, arithmetic operations with the spectral bands were calculated using the “Raster Calculator” obtaining the indices NormNIR, Normalized Difference Vegetation Index (NDVI), Green-Red NDVI (GRNDVI), and Green NDVI (GNDVI), the values of which on average for healthy leaves were: 0.66; 0.64; 0.32, and 0.55 and for infested leaves: 0.53; 0.41; 0.06, and 0.37 respectively. The analysis concluded that healthy leaves presented higher values of VIs when compared to infested leaves. The index GRNDVI was the one that better differentiated infested leaves from the healthy ones. Full article

A combine harvester has been widely employed for harvesting paddy in Malaysia. However, it is one of the most challenging machines to operate when harvesting grain crops. Improper handling of a combine harvester can lead to a significant amount of grain loss. Any losses during the harvesting process would result in less income for the farmers. Grain loss sensing technology is automated, remote, and prospective. It can help reduce grain losses by increasing harvesting precision, reliability, and productivity. Monitoring and generating real-time sensor data can provide effective combine harvester performance and information that will aid in analyzing and optimizing the harvesting process. Thus, this paper presents an overview of the conventional methods of grain loss measurements, the factors that contribute to grain losses, and further reviews the development and operation of sensor components for monitoring grain loss during harvest. The potential and limitations of the present grain loss monitoring systems used in combine harvesting operations are also critically analyzed. Several strategies for the adoption of the technology in Malaysia are also highlighted. The use of this technology in future harvesting methods is promising as it could lead to an increase in production, yield, and self-sufficiency to meet the increasing demand for food globally. Full article

Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r² = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r² = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry. Full article

The development of thermal energy storage solutions (TES) in agroindustry allows reduction of production costs and improvement of operation sustainability. Such solutions require high storage capacity and the ability to adapt to existing equipment. The use of phase change materials (PCMs), which are able to store thermal energy as latent heat, creates new opportunities for heat storage solutions (LHS, latent heat storage) with higher energy density and improved performance when compared to sensible heat storage. New architectures are envisaged where heat storage is distributed throughout the production chain, creating prospects for the integration of renewable generation and recovery of industrial heat waste. This work aims to investigate the benefits of decentralized thermal storage architecture, directly incorporating PCM into the existing equipment of an agroindustry production line. To assess the feasibility and potential gain in the adoption of this TES/LHS distributed solution, a tempering and mixing equipment for food granules is selected as a case study, representing a larger cluster operating under the operation paradigm of water jacket heating. The behavior of the equipment, incorporating an inorganic PCM, is modeled and analyzed in the ANSYS Fluent software. Subsequently, a prototype is instrumented and used in laboratory tests, allowing for data collection and validation of the simulation model. This case study presents a demonstration of the increase in storage capacity and the extension of the discharge process when compared to a conventional solution that uses water for sensible heat storage. Full article

UAV may be limited by its flight height and camera resolution when aerial photography of a tea garden is carried out. The images of the tea garden contain trees and weeds whose vegetation information is similar to tea tree, which will affect tea tree extraction for further agricultural analysis. In order to obtain a high-definition large field-of-view tea garden image that contains tea tree targets, this paper (1) searches for the suture line based on the graph cut method in the image stitching technology; (2) improves the energy function to realize the image stitching of the tea garden; and (3) builds a feature vector to accurately extract tea tree vegetation information and remove unnecessary variables, such as trees and weeds. By comparing this with the manual extraction, the algorithm in this paper can effectively distinguish and eliminate most of the interference information. The IOU in a single mosaic image was more than 80% and the omissions account was 10%. The extraction results in accuracies that range from 84.91% to 93.82% at the different height levels (30 m, 60 m and 100 m height) of single images. Tea tree extraction accuracy rates in the mosaic images are 84.96% at a height of 30 m, and 79.94% at a height of 60 m. Full article

Turbulence phenomena created around a greenhouse due to different wind loads are key factors in its structural design and significantly affect the ventilation rates through its side and roof openings. Using the turbulence models of ANSYS FLUENT software to investigate the airflow around an arched-roof-greenhouse-shaped obstacle placed inside a wind tunnel was the aim of this study. Velocity and pressure areas around the obstacle were examined by selecting three different turbulence models (Standard, RNG and Realizable k–ε models) under three different airflow entry velocities (0.34, 1.00 and 10.00 m s⁻¹) in the wind tunnel. All k–ε models showed that when the air velocity was intensified, the airflow was identified as turbulent. The horizontal velocity profile predicted more accurately the presence of vortices in contrast with the vector sum of the perpendicular velocity components. Vortices were formed upstream, above the roof and downstream of the obstacle, and the applied models showed that when airflow velocity increases, the size of the upstream vortex decreases. Finally, there was a strong indication from the modeling results that the vortex on the roof of the obstacle was an extension of the vortex that was created downstream. Full article

Digital agriculture is fundamental to potential improvements in the field by optimizing processes and providing intelligent decision making. This study aims to calculate the height and canopy diameter of recently transplanted coffee plants over three periods of crop development using aerial images, verify statistical differences between field measurements and aerial images, estimate linear equations between field data and aerial images, and monitor the temporal profile of the growth and development of the cultivar understudy in the field based on information extracted from aerial images through a Remotely Piloted Aircraft System (RPAS). The study area comprises a recently transplanted five-month-old Coffea arabica L. cultivar IAC J10 with information of height and crown diameter collected in the field and aerial images obtained by RPAS. As a result, it was possible to calculate the height and diameter of the canopy of coffee plants by aerial images obtained by RPAS. The linear estimation equation for height and crown diameter was determined with satisfactory results by coefficients R and R² and performance metrics MAE, RMSE, and regression residuals, and it was possible to monitor the temporal profile of the height of the coffee cultivar in the field based on aerial images. Full article

Proper climatic conditions in greenhouses are one of the major parameters to ensure optimum crop development. The installation of heating and cooling systems are the common solution to form a proper microclimate inside the greenhouse. However, the operation of these systems is accompanied by energy consumption. Therefore, many methods and alternative systems are sought to encounter this issue. A system which has been examined as an alternative solution for full or partial cover of a greenhouse is the Earth to Air Heat Exchanger (EAHE). Up to now, many research works have concentrated on the investigation and operation of such systems. In this study, a method to enhance the efficiency of the EAHE is examined based on the simultaneous flow of water (Water assisted earth to air heat exchanger—WAEAHE) following the concept of a double pipe heat exchanger which has been widely used in other applications. Furthermore, the improvement of the systems’ efficiency is investigated via the application of fins on the internal pipe of the heat exchanger. For the purpose of the study, different case studies have been investigated in order to reach safe results conserving the parameters affecting its efficiency. The results of the theoretical analysis have shown that the application of an internal water pipe can increase the system’s efficiency sufficiently, while it is further increased with the application of fins. In fact, the application of fins can lead to an increase of the overall heat transfer coefficients varying from 36–68%. In the current study, only the energy efficiency of the system was estimated. This system needs to be further investigated to be technically and financially efficient and applicable in actual case studies. Full article

For its small square footage, a vertical bed biofilter was developed for odor emission mitigation for livestock facilities with limited area available for biofilter installation. However, a concern about the design is that airflow and moisture may be poorly distributed across the biofilter due to the effects of gravity. Relevant data are sporadic in the literature. To fill the knowledge gap, two vertical bed biofilters were constructed at a university swine facility and monitored for two months. The monitoring was taken at 27 grid points on each biofilter per field visit. Results revealed that both the airflow and medium moisture content were unevenly distributed. The sun-facing side of the biofilters had significantly lower medium moisture content (p < 0.01) due to solar-induced water evaporation. The side directly facing the barn exhaust had the highest airflow. Airflows varied along the height of the biofilters, but no significant difference was noted. The uniformity of airflow and moisture content, characterized by coefficient of variance (CV) and distribution uniformity (DU) respectively, were examined over the monitoring campaign. Possible reasons for uneven distribution were explored and recommendations are made to address the uniformity issue. The findings from the study are expected to further the development and implementation of biofiltration technology for livestock odor control. Full article

The goal of this research was to compare and evaluate the measurements taken by different instruments regarding alterations while varying the ultraviolet (UV)-blocking property of cladding material during its usage under real greenhouse conditions. The UV-blocking covering material, low-density polyethylene (LDPE), is enriched with additives that are scattered in several layers during the manufacturing process, resulting in the reinforcement of its properties mechanically as well as optically. The duration of this study was three years, and the instruments used were: (a) sensors measuring the UV radiation reaching the earth’s surface in its A and B components; and (b) a portable spectroradiometer capable of measuring the transmissivity of a material, only in the UV-A region. Three covering materials were used with different UV radiation transmissivity. The transmittance was measured both in the laboratory (on samples taken from the roof) and in the field (where the greenhouses were located). Equations were defined to describe the variation in UV radiation transmission increase rate as a function of field exposure time. Lastly, it is important to note that the specific UV radiation sensors were extremely accurate. Full article

Among its many functions, soil represents the active natural medium for plant growth. Different soils have various structural characteristics, that correspond to their qualitative parameters in terms of physical, chemical, and biological fertility. Because of their extremely slow formation processes, soils are also a non-renewable resource, easily subject to degradative processes. Among their mineral constituents many agrarian soils present, in addition to the fine earth, variable percentages of coarse fractions in their arable layer, which interfere with the crop growth, requiring more energy to manage cultivation operations, and damaging the machinery up to making its use impractical. In these conditions, it becomes necessary to proceed with the soil destoning, particularly for the management of Precision Farming techniques. Depending on the percentages, sizes and types of coarse fractions, the soil destoning systems concern: (i) the collection and removal of stones from the field, (ii) the on-site stones crushing, and (iii) the stone burial. In this article, we report the first evaluation of a deep burial destoning system carried out in the CREA Experimental Center of Treviglio (Italy). With the described reclamation system, a significant long-term improvement of soil quality in a 600 mm thick arable layer was achieved; avoiding the shortcomings of the destoning systems as commonly applied in agricultural lands. Full article