AgriEngineering, Volume 5, Issue 4 (December 2023) – 50 articles

Remotely piloted aircraft systems (RPASs) have gained prominence in recent decades primarily due to their versatility of application in various sectors of the economy. In the agricultural sector, they stand out for optimizing processes, contributing to improved sampling, measurements, and operational efficiency, ultimately leading to increased profitability in crop production. This technology is becoming a reality in coffee farming, an essential commodity in the global economic balance, mainly due to academic attention and applicability. This study presents a bibliometric analysis focused on using RPASs in coffee farming to structure the existing academic literature and reveal trends and insights into the research topic. For this purpose, searches were conducted over the last 20 years (2002 to 2022) in the Web of Science and Scopus scientific databases. Subsequently, bibliometric analysis was applied using Biblioshiny for Bibliometrix software in R (version 2022.07.1), with emphasis on the temporal evolution of research on the topic, performance analysis highlighting key publications, journals, researchers, institutions, countries, and the scientific mapping of co-authorship, keywords, and future trends/possibilities. The results revealed 42 publications on the topic, with the pioneering studies being the most cited. Brazilian researchers and institutions (Federal University of Lavras) have a strong presence in publications on the subject and in journals focusing on technological applications. As future trends and possibilities, the employment of technology optimizes the productivity and profitability studies of coffee farming for the timely and efficient application of aerial imaging. Full article

Facing severe climate change, preserving the environment, and promoting sustainable development necessitate innovative global solutions such as waste recycling, extracting value-added by-products, and transitioning from traditional to renewable energy sources. Accordingly, this study aims to repurpose fish waste into valuable, nutritionally rich products and extract essential chemical compounds such as proteins and oils using a newly developed hybrid solar dryer (HSD). This proposed HSD aims to produce thermal energy for drying fish waste through the combined use of solar collectors and solar panels. The HSD, primarily composed of a solar collector, drying chamber, auxiliary heating system, solar panels, battery, pump, heating tank, control panel, and charging unit, has been designed for the effective drying of fish waste. We subjected the fish waste samples to controlled drying at three distinct temperatures: 45, 50, and 55 °C. The results indicated a reduction in moisture content from 75.2% to 24.8% within drying times of 10, 7, and 5 h, respectively, at these temperatures. Moreover, maximum drying rates of 1.10, 1.22, and 1.41 kgH₂O/kg dry material/h were recorded at 45, 50, and 55 °C, respectively. Remarkable energy efficiency was also observed in the HSD’s operation, with savings of 79.2%, 75.8%, and 62.2% at each respective temperature. Notably, with an increase in drying temperature, the microbial load, crude lipid, and moisture content decreased, while the crude protein and ash content increased. The outcomes of this study indicate that the practical, solar-powered HSD can recycle fish waste, enhance its value, and reduce the carbon footprint of processing operations. This sustainable approach, underpinned by renewable energy, offers significant environmental preservation and a reduction in fossil fuel reliance for industrial operations. Full article

This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes. Full article

Climate change is linked to CO₂ emissions, the reduction of which has become a top priority. In response to these circumstances, scientists must constantly develop new technologies that increase fuel efficiency and reduce emissions. Agriculture today is dominated by arable fields of various sizes, shapes, and dimensions, and to achieve fuel economy and environmental impact requirements, it is not enough to know only the principles of optimization of tillage processes; it is also necessary to understand the influence of field size and its shape and dimensions on tillage performance. The purpose of this research is to present a methodology that allows predicting tractor fuel demand and CO₂ emissions per unit of ploughed area when ploughing field plots with different shapes and dimensions and to confirm a suitable variable for such a prediction. Theoretical calculations and experimental tests have shown that the field ploughing time efficiency coefficient is a useful metric for comparing field plots of different shapes and dimensions. This coefficient effectively describes tractor fuel consumption and CO₂ emissions during ploughing operations on differently configured field plots. A reasonable method for calculating the real field ploughing time efficiency coefficient is based on field and tillage data and a practical determination method using tractor engine load reports. It was found that during the research, when ploughing six field plots of different shapes and dimensions, with an area of 6 ha, the field ploughing time efficiency coefficient varied from 0.68 to 0.82, and fuel consumption between 15.6 and 16.5 kg/ha. In the field plot of 6 ha, where the field ploughing time efficiency coefficient was 15% higher, the fuel consumption per unit area was lower by about 5.5%. The results of this study will help to effectively predict tillage time and tractor fuel consumption required for different field shapes and dimensions. Full article

Crown rot, caused by Fusarium species, is the most devastating postharvest disease in bananas. Fungicides are traditionally applied as a postharvest treatment to control crown rot in bananas. However, there is a need to research environmentally friendly compounds as postharvest treatments instead of chemical fungicides. The phenolic compounds gallic acid, protocatechuic acid, and chlorogenic acid were identified in coconut mesocarp extract. Overall, the treatments were more efficient in crown-based than fruit-based culture mediums. The mycelial development was inhibited in a range from 20 to 26% (applying coconut mesocarp extract at 5%) compared to the control. Sporulation and spore germination were significantly inhibited, with a reduction of 88% in spore production and 91% in spore germination inhibition compared to the control. In in vivo tests, the aqueous extracts were effective by limiting the percentage of infected fruit, crown rot, and fruit severity. The use of coconut mesocarp extracts can be an effective and environmentally friendly alternative to the use of fungicides for controlling Fusarium musae on bananas. Full article

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops. Full article

The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run. Full article

In this study, the finite volume method was used to evaluate the thermal performance of a flat-plate solar collector used to dry agricultural crops. A 3D numerical model was created and used to predict the outlet air velocities and temperatures for three inlet air velocities. When compared with experimental measurements, the numerical predictions showed good agreement under all testing conditions. Then, the numerical model was used to predict the internal airflow and heat transfer characteristics of the collector. The internal baffles were found to increase the dwell time and efficiency but also promote flow separation, which resulted in flow loss. In addition, the collector has a transparent cover that results in a substantial heat loss, which can be mitigated by adding a vacuum gap between the flow inside the collector and the cover. Increasing the flow rate increased the heat loss and decreased the heat uptake, which decreased the temperature difference between the inlet and outlet of the collector. Because the heat was lost through long-wavelength radiation via the transparent cover and sidewalls, coating the absorber plate with black matte paint to increase the solar radiation absorption coefficient did not improve the drying performance. Full article

The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide. Full article

This study was conducted to survey the level of technification of quail sheds in Brazil. Data from 25 quail farms (5 in each Brazilian region) were collected by image analysis of videos available on the Internet. The analyzed variables were farm location, degree of technological adoption in quail sheds, housing conditions, structural conditions, wall conditions, and thermal comfort equipment. The data were subjected to descriptive analysis, and differences were assessed using the chi-squared test (p < 0.10). It was found that curtain walls were the most used system for air entry and renewal in quail sheds. Fan systems were present in only 12% of sheds, and evaporative cooling systems (or air conditioning) were observed in 4% of sheds, exclusively on large farms. Internal insulation was used in 20.83% of farms. In conclusion, Brazilian quail sheds have a low degree of technification; about 90% do not use implements such as ceiling, ventilation, and cooling systems. These conditions make it difficult to control environmental variables within quail sheds, impairing thermal comfort and, consequently, animal welfare and quail productivity. Full article

The flavor, aroma, and color of coffee can be changed due to mechanical damage, reducing its quality. To measure the mechanical behavior of the fruit, compression tests can be performed at different stages of ripeness. In this study, we analyzed the deformation, strain energy, and von Mises stress of coffee fruits at mature, semi-mature, and immature stages under compression forces. Compression in three directions (x, y, and z) was simulated on coffee fruit models using the finite element method. A compression support was applied in the opposite direction to the force application axis. Numerical simulations of the compression process allowed us to verify that the more mature the fruit, greater the associated mean deformation (2.20 mm mm⁻¹, 0.78 mm mm⁻¹, and 0.88 mm mm⁻¹), the lower the mean strain energy (0.07 mJ, 0.21 mJ, and 0.34 mJ), and the lower the mean equivalent von Mises stress (0.25 MPa, 1.03 MPa, and 1.25 MPa), corresponding to ripe, semi-ripe, and immature fruits, respectively. These analyses not only save time and professional resources but also offer insights into how strain energy and von Mises stress affect fruits at different maturation stages. This information can guide machine adjustments to reduce coffee harvesting damages. Full article

Modeling and simulation have become fundamental tools for the microclimatic analysis of greenhouses under various climatic conditions. These models allow precise control of the climate inside the structures and the optimization of their performance under any situation. In Colombia, the availability of energy balance models adapted to local greenhouses and their climate is limited, which affects the decision-making of both technical advisors and growers. This study focused on calibrating and evaluating a dynamic energy balance model to predict the thermal behavior of an innovative type of plastic-covered greenhouse designed for the Bogotá savanna. The selected model considers fundamental heat and mass transfer processes, incorporating parameters that depend on the architecture of the structure and local climatic conditions, making it suitable for protected agriculture in Colombia. The results of the post-calibration evaluation showed that the model is highly accurate, with a temperature prediction efficiency close to 86%. This ensures that the model can accurately predict the thermal behavior of the greenhouse being evaluated. It is important to note that the model can also anticipate phenomena characteristics of Colombian greenhouses, such as thermal inversion. This advance has become a valuable tool for decision-making in protected agriculture in the region. Full article

Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than $97.0\%$ and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML. Full article

Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m⁻² s⁻¹ (light stress), and moderate natural light, 50 μmol photons m⁻² s⁻¹ (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R_666:682)/mean(R_552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data. Full article

The objectives set in the European Green Deal constitute the starting point of this review, which then focuses on the current implementation gap between agro-industrial wastes as resources for large-scale bioprocesses (e.g., baker’s yeast, bioethanol, citric acid, and amino acids). This review highlights the current lack of sustainability of the post-harvest processing of grapes and apples. In light of the European Green Deal, industrial biotechnology often lacks sustainability as well. We reviewed the recent progress reported in the literature to enhance the valorization of grape and apple pomace and the current failure to implement this research in technical processes. Nevertheless, selected recent papers show new perspectives to bridge this gap by establishing close collaborations between academic teams and industrial partners. As a final outcome, for the first time, we drew a circular flow diagram that connects agriculture post-harvest transformation with the industrial biotechnology and other industries through the substantial valorization of apple and grape pomace into renewable energy (solid biofuels) and sugar extracts as feedstock for large-scale bioprocesses (production of baker’s yeast industry, citric acid, bioethanol and amino acids). Finally, we discussed the requirements needed to achieve the successful bridging of the implementation gap between academic research and industrial innovation. Full article

Promotion of research and development in advanced technology must be implemented in agriculture to increase production in the current challenging environment where the demand for manual farming is decreasing due to the unavailability of skilled labor, high cost, and shortage of labor. In the last two decades, the demand for fruit harvester technologies, i.e., mechanized harvesting, manned and unmanned aerial systems, and robotics, has increased. However, several industries are working on the development of industrial-scale production of advanced harvesting technologies at low cost, but to date, no commercial robotic arm has been developed for selective harvesting of valuable fruits and vegetables, especially within controlled strictures, i.e., greenhouse and hydroponic contexts. This research article focused on all the parameters that are responsible for the development of automated robotic arms. A broad review of the related research works from the past two decades (2000 to 2022) is discussed, including their limitations and performance. In this study, data are obtained from various sources depending on the topic and scope of the review. Some common sources of data for writing this review paper are peer-reviewed journals, book chapters, and conference proceedings from Google Scholar. The entire requirement for a fruit harvester contains a manipulator for mechanical movement, a vision system for localizing and recognizing fruit, and an end-effector for detachment purposes. Performance, in terms of harvesting time, harvesting accuracy, and detection efficiency of several developments, has been summarized in this work. It is observed that improvement in harvesting efficiency and custom design of end-effectors is the main area of interest for researchers. The harvesting efficiency of the system is increased by the implementation of optimal techniques in its vision system that can acquire low recognition error rates. Full article

To expand the potential of renewable energies, energy storage is required to level peaks in energy demand and supply. The aim of the present study was to examine and characterize the energy consumption of a milk production system to find possibilities and boundaries for a self-sufficient energy system. A detailed quantification of energy production of the test farm and the consumption of the milk production system showed, that the total energy production could cover the energy consumption of the production process. However, the temporal distribution of energy production and consumption requires energy storage in the production process. Though ice bank milk cooling and water heating have the potential to cover parts of this storage capacity, battery storage is mandatory to enable full autarky. The consideration of different seasons leads to different optimal dimensions of the energy system. The energy price is decisive for profitability, both in the purchase and in the sale. Smaller energy systems are generally at an advantage due to the higher self-consumption quota. Full article

One of the most modern technologies for growing row crops is strip-till. Currently, it occupies an intermediate position between conventional tillage and no-till technologies. Special complex and expensive machines are used to implement strip-till technology practically. To avoid this, a combined unit is proposed, including a tractor and two simple machines: a front-disc harrow and a chisel plough mounted behind the tractor. As experimental studies have shown, this unit makes implementing the strip-till one-pass technology possible. In this case, the oscillations process in the soil-loosening depth of strips is low-frequency since at least 95% of this statistical parameter variance is concentrated in the frequency range of 0–16.8 s⁻¹ or 0–2.7 Hz, and its maximum falls at a frequency of 0.4 Hz. The soil-loosening depth in the strips can deviate from the mean value by ±2 cm once per 7.1 m of the combined unit’s path. With a mean speed of its movement of 2.1 m·s⁻¹, the release frequency of the mean value of the soil-loosening depth exceeding ±2 cm is only 0.29 s or 0.05 Hz. Not less than 95% of the loosened strips’ non-straightness oscillations variance is in the frequency range of 0–0.25 m⁻¹, and the value of the variance itself is small and amounts to 1.08 cm². Proceeding from this, the non-straightness of the loosened strips by the combined unit can be considered satisfactory since its indicators meet the requirements for the non-straightness of row crops in terms of variance and frequency oscillations. Full article

Social dynamics and lighting conditions influence floor egg-laying behavior (FELB) in hens. Hens prefer to lay eggs in darker areas, leading to mislaid eggs in cage-free systems. Consistent lighting is crucial to prevent mislaid eggs, but equipment obstructions can result in a dark floor area. These dark areas entice hens to lay their eggs outside the designated nesting area, which can lead to potential losses, damage, or contamination, creating hygiene problems and increasing the risk of bacterial growth, resulting in foodborne illnesses. Therefore, additional lighting in dark areas can be a potential solution. The objectives of this study were to evaluate the effectiveness of providing additional light in darker areas in reducing the number of mislaid eggs and FELB. Approximately 720 Hy-Line W-36 hens were housed in four cage-free experimental rooms (180 hens per room), and 6 focal hens from each room were randomly selected and provided with numbered harnesses (1–6) to identify which hens were performing FELB and identify the effect of illuminating solutions. Eggs laid on the floor and in nests were collected and recorded daily for two weeks before and after the light treatment. Statistical analysis was performed using paired t-tests for mislaid eggs and logistic regression for FELB in R Studio (p < 0.05). This study found that additional lighting in darker areas reduced the number of mislaid eggs by 23.8%. Similarly, the number of focal hens performing FELB decreased by 33.3%. This research also unveiled a noteworthy disparity in FELB, with approximately one-third of hens preferring designated nesting areas, while others opted for the floor, which was influenced by social dynamics. Additionally, egg-laying times varied significantly, ranging from 21.3 to 108.03 min, indicating that environmental factors and disturbances played a substantial role in this behavior. These findings suggest that introducing additional lighting in darker areas changes FELB in hens, reducing mislaid eggs and improving egg quality in cage-free systems. Full article

Gum rosin is an important agricultural commodity which is widely used as a raw material for various industries. However, gum rosin has low stability, crystallizes easily, and tends to oxidize. This is due to carboxyl groups and conjugated double bonds in gum rosin’s structure. Therefore, to reduce these weaknesses, it is necessary to modify the rosin compound to achieve better stability via the esterification process. This paper surveys esterification agents such as glycerol, pentaerythritol, methanol, ethylene glycol, polyethylene glycol (PEG), allyl group, and starch Rosin ester. The product is used in the manufacture of pressure-sensitive adhesives, drug delivery, solder flux for electronic devices, as a plasticizer, and as a coating agent in fertilizers. In general, the esterification reaction between alcohols and carboxylic acids is very slow without a catalyst. Heterogeneous catalysts have the advantage of controlling size, structure, spatial distribution, surface composition, thermal-chemical stability, and selectivity. Among the catalysts for gum rosin esterification are ZSM-5, Fe₃O₄, ZnO, Calcium, TiO₂, Kaolin, and Al₂O₃, among others. Different catalysts and esterification agents can produce various physical and chemical properties of rosin ester and will result in specific rosin ester products, such as glycerol ester, pentaerythritol ester, methyl ester, glycol ester, allyl ester, and acid starch-based rosin. Full article

In this study, the objective was to ascertain the optimal extraction method for the recovery of polyphenols from two peach cultivars, namely ‘Andross’ and ‘Everts’, at unripe and ripe stages. Two extraction techniques were explored: conventional extraction and cloud-point extraction (CPE), utilizing various solvents, including water, ethanol, 60% ethanol, and the surfactant Tween 80. Moreover, the conditions of CPE (such as pH, ionic strength, surfactant concentration, etc.) were optimized. To elucidate the antioxidant activity of the extracts, the total polyphenol content (TPC), the ferric-reducing antioxidant power (FRAP) assay, and the DPPH antiradical scavenging were measured. Our findings indicate that CPE is a superior method for polyphenol recovery. Unripe fruits had more antioxidants than ripe ones. Unripe ‘Andross’ fruit has a TPC of 1465.32 mg gallic acid equivalents per kilogram (mg GAE/kg). FRAP and DPPH levels were 7.33 and 5.12 mmol ascorbic acid equivalents (AAE/kg), respectively. With a TPC of 1714.53 mg GAE/kg, the unripe fruit from the ‘Everts’ cultivar has even more antioxidant capacity. Additionally, its FRAP and DPPH values were increased at 8.57 and 6.08 mmol AAE/kg, respectively. These findings underscore the efficacy of CPE as a preferred method for polyphenol extraction while also highlighting the enhanced antioxidant potential of unripe peaches, particularly in the ‘Everts’ cultivar. Full article

Developing cotton ginning methods that improve fiber length uniformity index to levels that are compatible with newer and more efficient spinning technologies would expand market share and increase the demand for cotton products and give U.S. cotton a competitive edge to synthetic fibers. Older studies on lint cleaning machines showed that the most widely used feed mechanism that places fiber on the cleaning cylinder damages the fiber and reduces uniformity. The present study evaluates how conventional and experimental feed mechanisms affect uniformity. The lint cleaners were used with both saw and roller gin stands. Four diverse cotton cultivars from the Far West, Southwest, and Mid-South were used in the test. Statistical analysis used a random effects modeling approach which included constructing a 95% confidence interval for each ginning treatment around the predicted mean for the fiber property of interest, and then examining which treatments overlap (for comparison). Results show that the micro-saw gin with the direct-feed lint cleaner had the best uniformity at 85.8%. Prior research has shown that roller ginning is consistently higher in uniformity than any type of saw ginning. In this study, the roller ginning treatments had uniformities of 85.3 and 85.6%, so it is encouraging that the saw gin stand with the direct-feed lint cleaner had very high uniformity. This suggests that it may be beneficial to place fiber directly onto the lint cleaning saw without changing direction. Additionally, the saw gin-coupled lint cleaner had a uniformity of 84.3% which is also a respectable level of uniformity. These results indicate that the direct-feed lint cleaner and coupled lint cleaner warrant further testing under better controlled conditions. Full article

This paper presents the implementation of statistical and remote sensing techniques to analyze the spectral response, grain yield, and infestation of fall armyworm (Spodoptera frugiperda) in corn (Zea mays) based on the application of edaphic and foliar treatments with silicon, comparing the results with those reported in the literature where it has been demonstrated that the incorporation of this nutrient in different crops improves the activity of the enzyme nitrate reductase and dry matter weight gain. The results show that the foliar application of silicon tends to increase grain production in the crop, while the soil treatment does not improve yield. Similarly, foliar silicon application improves the Normalized Difference Vegetation Index, which improves plant health and could be correlated with higher grain yield of the crop. An inverse correlation was detected between the use of foliar silicon and the Normalized Difference Water Index and a direct relationship in the case of direct field application. As for the analysis of the data to verify the influence of the use of silicon on fall armyworm infestation, no statistically significant evidence was found that would lead to the conclusion that the application of this element, whether in soil or foliar form, could lead to a decrease in crop infestation. Full article

The occurrence of late spring frosts due to climate change causes great damage to plantation production worldwide. The main objective of the paper is to provide a comprehensive overview of the problem and to outline effective protective measures against late spring frosts. The nature of frost depends on regional, altitudinal, and geographic differences, but they all share a common problem: they remove heat, resulting in the freezing of new plant growth and flowers. Tissue freezing is affected by critical temperatures and the frost type, intensity, and duration. Protection against late spring frosts can be broadly divided into three categories: active, passive, and chemical measures. In the field of agricultural engineering, various techniques have been thoroughly researched, and their effectiveness has been confirmed by research. These include various sprinkler systems, different heating devices, and large-diameter fans. Conclusive findings are being made on the performance of these systems in sub-zero temperatures and their cost-effectiveness. Climate change increases the importance of protecting permanent crops from late spring frosts and requires advances in agricultural technology to meet changing production demands and challenges. Full article

In banana cultivation, a considerable amount of the production is wasted every year because of various constraints present in the post-harvest management chain. Converting green banana pulp and peels into flour could help to reduce losses and enable the food sector to keep the product for an entire year or more. In order to use green banana fruit and peel flour in the food industry as a raw ingredient such as in bakery and confectionery items—namely biscuits, cookies, noodles, nutritious powder, etc.—it is essential to standardize the process for the production of the flour. As a result, the purpose of this study was to investigate the influence of pretreatment and temperature on the drying capabilities and quality of dried green banana peel. The green banana peel pieces were pretreated with 0.5 and 1.0% KMS (potassium metabisulfite), and untreated samples were taken as control, and dried at 40°, 50°, and 60 °C in a tray dryer. To reduce the initial moisture content of 90–91.58% (wb) to 6.25–9.73% (wb), a drying time of 510–360 min was required in all treatments. The moisture diffusivity (D_eff) increased with temperature, i.e., D_eff increased from 5.069–6.659 × 10⁻⁸, 6.013–7.653 × 10⁻⁸, and 4.969–6.510 × 10⁻⁸ m²/s for the control sample, 0.5% KMS, and 1.0% KMS, respectively. The Page model was determined to be the best suited for the drying data with the greatest R² and the least χ² and RSME values in comparison with the other two models. When 0.5% KMS-pretreated materials were dried at 60 °C, the water activity and drying time were minimal. Hue angle, chroma, and rehydration ratio were satisfactory and within the acceptable limits for 0.5% KMS-pretreated dried banana peel at 60 °C. Full article

The use of unmanned aerial vehicles (UAVs) for pesticide application has increased substantially. However, there is a lack of technical information regarding the optimal operational parameters. The aim of this study was to evaluate the quality of pesticide application on a soybean crop using a UAV employing different spray nozzles. The experiments were conducted using a completely randomized design with four treatments and eight repetitions. The trial was conducted in a soybean growing area during the soybean reproductive stage (1.1 m tall). The treatments included aerial application (rate: 10 L hm⁻²) using an Agras MG1-P UAV with XR 11001 (flat fan), AirMix 11001 (air-induction flat fan), and COAP 9001 (hollow cone spray) nozzles; for comparison, ground application (rate of 100 L hm⁻²) using a constant pressure knapsack sprayer with an XR 110015 (flat fan) nozzle was performed. The deposition was evaluated by quantifying a tracer (brilliant blue) using spectrophotometry and analyzing the droplet spectrum using water-sensitive paper. Furthermore, the application quality was investigated using statistical process control methodology. The best deposition performance was exhibited by the application via UAV using the COAP 9001 and AirMix 11001 nozzles. For all the treatments, the process remained under statistical control, indicating commendable adherence to quality standards. The aerial application provided greater penetration of the spray into the crop canopy. With the use of the UAV, the coverage on the water-sensitive paper was <1%; moreover, the AirMix 11001 and XR 110015 nozzles had the lowest drift potential. Full article

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R² > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models. Full article

The effects of pulsed electric field treatment and thermal pasteurization on the microbial and physical properties of a high-fiber, nutritional milk-based beverage made with date powder were studied. Four ratios of date powder (10, 15, 20, and 25 w/w) were added to the milk, which was then kept at 5 °C for 6 days for the thermal pasteurization and the control treatments. The pulsed electric field treatment had three levels of pulses (20, 50, and 80 pulses) and four ratios of date powder, 10, 15, 20, and 25% (w/w), and then kept at 5 °C for 6 days. The samples were evaluated for the pH, total soluble solids (TSS), total color difference (ΔE), and total viable count (TVC) during their shelf life. The pH values of the beverages in the control treatment were 5.58, 5.45, 5.33, and 5.29 and 6.68, 6.48, 6.26, and 5.87 in the thermal treatment after 6 days, with powder ratios of 10, 15, 20, and 25% (w/w), respectively. The pH values of the beverages in the pulsed electric field treatment were 6.8, 6.64, 6.56, and 6.28 at 80 pulses after 6 days, with powder ratios of 10, 15, 20, and 25% (w/w), respectively. The TVCs in the control treatment were 6.2, 5.44, 4.5, and 3.94 log10 CFU/mL and 4.02, 3.92, 3.54, and 3.31 log10 CFU/mL in the thermal treatment after 6 days, with powder ratios of 10, 15, 20, and 25% (w/w), respectively. The TVCs of the beverages in the pulsed electric field treatment were 1.53, 1.11, 0.665, and 0.511 log10 CFU/mL at 80 pulses after 6 days, with powder ratios of 10, 15, 20, and 25% (w/w), respectively. This shows that following treatment with a pulsed electric field at 80 pulses, a milk-based drink with date powder and no preservatives can be kept at 5 °C for up to 6 days. Full article

An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security. Full article

Drying is a crucial and energy-consuming process in black tea manufacturing that is aimed at reducing moisture content and terminating enzymatic reactions in tea leaves. In Sri Lanka, an endless chain pressure (ECP) dryer is commonly used for drying, but it consumes a significant amount of energy, necessitating the optimization of drying conditions. The current drying temperature at the Houpe tea factory in Ratnapura, Sri Lanka is 121 °C (250 °F), and it has not been optimized for a considerable period. As a result, energy consumption and wastage are high, leading to an inferior quality of black tea. To optimize factory conditions, tea leaves were dried under different temperatures: 115 (T1), 118 (T2), 121 (T3), 124 (T4), and 127 (T5) °C. Energy consumption, energy wastage, and specific energy consumption (SEC) for tea drying were calculated. Additionally, chemical and sensory analyses of samples of made tea were performed. SEC and energy wastage were significantly (p < 0.05) lower for treatments T1 and T2 than for other treatments. The theaflavin and thearubigin contents were significantly (p < 0.05) higher while total phenolic content was moderate for treatment T2. The sensory parameters of T2 outperformed other treatments. Based on these results, the optimum drying temperature for the ECP dryer was determined to be 118 °C and this temperature has been recommended for this factory. Full article