Abstract This paper investigates the effect of origin, harvest times and loading rates on the biome chanical properties of laver, aiming to develop laver harvesting and postharvest transportation equipment. The values and changing regular of biomechanical properties were obtained via a combi nation of morphological and mechanical tests as well as numerical statistics. The correlation between biological and mechanical properties was detected simultaneously. The results show that the biolog ical properties are affected dramatically by origin and harvest times. The values of length, width,thickness and mass of laver from Dalian exceeded those found in Qingdao and Lianyungang. The width, thickness and mass increased, whereas the length-to-width ratio decreased with the increasing harvest time. Meanwhile, the mechanical properties are also influenced significantly by loading rates,origin and harvest times. Tensile and shear strength displayed an overall decreasing trend, whereas adhesive force and adhesiveness in general increased with the increasing loading rate. The tensile and shear strengths were greatest for laver from Qingdao, while the adhesive force and adhesiveness were greatest for laver from Dalian. Tensile strength, adhesive force and adhesiveness increased, and shear strength decreased with the delay of harvest time. In addition, the tensile strength and thickness of the laver at different harvest times were positively correlated. The maximum tensile strength, shear strength, adhesive force and adhesiveness were 3.56 MPa, 4.79 MPa, 0.32 N and 1.01 N·mm, respectively. These results are believed to be able to provide a reference for the design and optimization of machineries such as harvest, postharvest transportation and laver processing.

Abstract Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies.We developed four common machine learning (ML) classifiers, the k-mean, naïve Bayes, support vector machine, and feed-forward artificial neural network (FANN), all of which were aimed at classifying the ripeness stage of mangoes at harvest. The ML classifiers were trained on biochemical data and then tested on physical and electrical data .The performance of the ML models was compared using fourfold cross validation. The FANN classifier performed the best, with a mean accuracy of 89.6% for unripe, ripe, and overripe classes, when compared to the other classifiers.

Abstract Japanese agriculture is facing a decrease in agricultural workers. Mechanization, both to save time and reduce physical input, is essential to solving this issue. Recent worldwide progress in Internet-of-things technology has enabled the application of remote-controlled and unmanned machinery in agriculture. This study was conducted in the Gojo-Yoshino mountainous region in Nara, Japan, which is famous for its persimmon cultivation. The performance of newly introduced smart agricultural machinery was studied in the field by simulating cultivation work. The results showed that the remote-control weeder, speed sprayer, and remote-control mini crawler carrier saved 90%, 75%, and 5% of weeding, spraying, and harvesting times, respectively, when compared with conventional methods. Such time savings led to an 8% decrease in the total working time spent on persimmon cultivation. In addition, using the speed sprayer showed improvement in the fruit’s quality. Results of the power assist suits did not show a time-saving effect but showed a reduction of physical burden. These results suggest that the mechanization of persimmon cultivation is efficient and labor-saving, and satisfies the need for farmers. However, the high investment costs remain an issue in extending mechanization to the region.

Abstract This work proposes the experimentation of an innovative hydraulic dredge for clam fishing (Chamelea gallina) in the Adriatic Sea (Italy). This innovative gear aimed at increasing the selectivity of the typical hydraulic dredge used currently, while at the same reducing the impact on benthos through the conception, installation, and experimentation of innovative technological solutions, consisting mainly of a vibrating bottom panel on the dredge and a “warning device” on the dredge mouth. Comparative experiments of the traditional vs. the modified gear, employing two boats fishing in parallel on the northern coast of Abruzzi (Adriatic Sea) and contrasting the catch with both paired comparisons and through modelling, showed that the innovative hydraulic dredge retains fewer undersize clams while yielding similar amounts of commercial product, moreover of higher quality; at the same time, it takes on board less discard, and catches significantly less vagile fauna. In short, the innovative gear is gaining five times over a list of six arameters considered as positive and/or advantageous for the clam fishery. The results allow proposals of potential improvements to clam-fishing instruments to make the selection processes more effective while promoting a lower impacting fishery, which is essential for clam management.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops. However, disease screening is challenging as there are no clear visible symptoms on upper stems and leaves at early growth stages. The current manual screening method requires experts to observe the crown and roots of plants to detect disease, which is time-consuming, subjective, labor-intensive, and costly. As digital color imaging has the advantages of low cost and easy use, it has a high potential to be an economical solution for crown rot detection. In this research, a crown rot disease detection method was developed using a smartphone camera and machine learning technologies. Four common wheat varieties were grown in greenhouse conditions with a controlled environment, and all infected group plants were infected with crown rot without the presence of other plant diseases. We used a smartphone to take digital color images of the lower stems of plants. Using imaging processing techniques and a support vector machine algorithm, we successfully distinguished infected and healthy plants as early as 14 days after disease infection. The results provide a vital first step toward developing a digital color imaging phenotyping platform for crown rot detection to enable the management of crown rot disease effectively. As an easy-access phenotyping method, this method could provide support for researchers to develop an efficiency and economic disease screening method in field conditions.

Abstract The exploitation and harnessing of renewable energies are becoming increasingly important throughout the world. This study presents a method of methane (CH4 ) generation using biological disintegration of food waste (FW) by anaerobic digestion (AD). The CH4 production was enhanced by the addition of three different types of iron oxide (Fe3O4 ) nanoparticles (NPs) (Cetyletrimethlebromide (CTAB), urea-capped Fe3O4 NPs and Fe3O4 NPs without capping). The bio generation of CH4 and biodegradation of volatile solids (VS) were carried out in an AD treatment at mesophilic conditions (35–37 ◦C) for more than 50 days in batch mode. The concentration of all three types of NPs was kept constant at 75 mg/L. It was noticed that urea-capped NPs produced the maximum CH4 (5.386 L), followed by Fe3O4 NPs (5.212 L). Methane production in the control bioreactor was 2.143 L .The experimental results of CH4 generation (a dependent variable) were analyzed against the concentrations of NPs used (as independent variables) in multiple regression analysis (MRA). The overall model for the experiments resulted in R2 and R-adjusted values of 0.995 and 0.993, respectively.

Abstract The insufficient supply of electrical energy, in addition to frequent disturbances and interruptions, has motivated the inclusion of solar, biogas, biomass or wind energy systems in many Brazilian farms. However, there are few studies that have addressed the technical and economic impacts of renewable sources for generating electricity in rural applications, leading farmers not to invest in these technologies for fear of financial losses. This study was carried out to evaluate the potential of grid-connected photovoltaic systems for supplying the electricity demand in dairy farms located at Minas Gerais State, Brazil. The electricity generated by grid-connected photovoltaic systems was estimated from global solar radiation measurements, considering six municipalities of Minas Gerais State, Brazil. Electricity consumption was monitored monthly during one year in 12 farms. The average percentages of electricity consumption in the main operations executed at farms were 4, 27, 12, 33 and 24% for lighting, milking, cleaning/disinfection (water heating and pumping), milk cooling/refrigeration and miscellaneous, respectively. The monthly differences between the electricity generation and consumption for the studied municipalities demonstrated the technical feasibility of grid-connected systems installed directly in the dairy farms, helping to achieve energy sustainability.

Abstract The purpose of this study was to evaluate the impact of different environmental factors such as temperature, solar radiation, and relative humidity on the quality of strawberries in terms of their shape, size, and sugar accumulation. The experiment was carried out in a small walk-in greenhouse in Matsusaka city, Japan. Harunoka strawberries (Fragaria × ananassa Duch.) were cultivated from September to May of the following year. Production was evaluated on 20 February 2021 (peak season) and 5 April 2021 (end season). To evaluate the influence of environmental factors on strawberry fruit quality, the weight, shape, and soluble sugar content were recorded and compared to each other. According to the environmental data, the average temperature between day and night at peak harvest was around 12 ◦C, which was suitable for high-quality strawberry cultivation. However, the average temperature difference between day and night was approximately 4 ◦C at the end of the season. In addition, there were no significant differences in solar radiation and relative humidity between both seasons. Increasing temperatures led to the decline in the soluble sugar content at the end season. Thus, it can be concluded that the temperature difference between day and night is a major factor affecting strawberry production. The assessment of the impact of environmental conditions on strawberry quality can be used as a guideline not only in temperate climates, but also in other climates, such as in tropical countries.

Abstract Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

Abstract Background: Medically ill youth are at increased suicide risk, necessitating early detection.This study aimed to assess the feasibility of administering the Death Implicit Association Test (Death IAT) to pediatric medical inpatients. Methods: Participants completed measures including the Ask Suicide-Screening Questions (ASQ) and the Death IAT. Results: Over 90% of participants found the Death IAT to be acceptable and more than 75% of participants were comfortable completing the task. There was a small, but statistically significant, improvement from pre-survey to post-survey reports of mood (t(174) = 3.02, p = 0.003, d = 0.15). Participants who endorsed a past suicide attempt on the ASQ had significantly higher “suicide” trial D-scores than those without a past suicide attempt (Wilcoxon W = 1312; p = 0.048; d = 0.61). Conclusions: Implementing an IAT measure among pediatric medical inpatients was feasible and acceptable. In exploratory analyses, “suicide” trial IAT D-scores were associated with past suicide attempts,suggesting future studies should examine whether implicit measures may be useful in hospital settings to augment detection of youth suicide risk.