AgriEngineering, Volume 5, Issue 1 (March 2023) – 41 articles

Tank mixing of pesticides, a common practice in many countries, when performed incorrectly, can negatively impact the effectiveness of the pesticides. This study aimed to investigate the physical–chemical properties, droplet spectrum, and absorption by soybean plants of mixtures of the azoxystrobin fungicide with glyphosate herbicide and different adjuvants (mineral oil, propionic acid, and orange oil). The study design was completely randomized, with five treatments (T): T1, only fungicide; T2, fungicide + glyphosate; T3, fungicide + mineral oil; T4, fungicide + propionic acid; and T5, fungicide + orange oil. The spray concentrations simulated an application rate of 160 L ha⁻¹ using the TT110015 nozzle. The physical stability, hydrogen ion potential, electrical conductivity, surface tension, droplet spectrum, and azoxystrobin absorption in soybean plants were evaluated. To measure absorption, soybean plants were sprayed at the reproductive growth stage, and leaf samples were collected after 0, 2, and 48 h. The amount of azoxystrobin absorbed was determined using gas chromatography. The results indicated that the spray mixtures were physically compatible. All mixtures produced a medium droplet spectrum. T2 had the lowest absorption percentages, suggesting that these pesticides should not be mixed. Adjuvants increased the amount of azoxystrobin absorbed by the plants, and it took 2 h on average for the soybean leaves to absorb 72.58% of the applied spray. Full article

The level of garlic consumption in Indonesia increases as the population grows. This is because most of the ingredients of Indonesian food recipes contain garlic. However, local garlic production is not sufficient to fulfil the demand. Therefore, the Indonesian government imported garlic from other countries to fulfil the demand. To reduce the import capacity of garlic, the government made a regulation to increase the potential area for garlic cultivation in several priority locations in Indonesia, one of which is Sembalun District, East Lombok. To support government regulation, this study presents an application of machine learning and a statistic approach for the garlic field mapping method in Sembalun, Indonesia. This study comprises several steps including the Sentinel-1A images data acquisition, image preprocessing, machine learning and statistic model training, and model evaluation. k-nearest neighbor (k-NN) and maximum likelihood classification (MLC) methods are selected in this study. The performance of k-NN and MLC are compared to other garlic field classification results developed in previous studies using pixel-based and image-based classifications. The comparison results show that the k-NN classification is slightly better than the SVM classification and also that it outperformed the MLC method. In addition, MLC works faster than k-NN in learning the dataset and testing the models. The classification results can be used to estimate garlic production in the study area. The study concludes that the proposed methods are better than other classification models and the statistic approach. The future study will improve dataset quality to increase the model’s accuracy. Full article

This work aimed to evaluate the effect of calcium on the development of corn plants grown with the omission and excess of calcium in a nutrient solution. The experiment was conducted in a greenhouse from March to May 2012. Three concentrations of calcium (0, 200, and 600 mg Ca L⁻¹) were added to the nutrient solution, which was renewed weekly, for a total of 40 days. The following variables were measured weekly: the number of leaves, average stem diameter, dry weight of the plant shoots and roots, and visual leaf diagnosis. The results showed that when the plants were deprived of calcium, their root systems were significantly reduced, as determined by the Tukey test (p ≤ 0.05). The plants with calcium deprivation had shorter roots and a dark brown color and displayed initial symptoms of chlorosis in their young leaves, which eventually led to necrosis and tipping. Hydroponics is promising and has shown satisfactory production results, contributing to the improvement of the environment, job creation, and increased profit for rural producers. Full article

The leaf extracts of Gymnema sylvestre consist of secondary metabolites which are well known for antioxidant activity. This study aimed to measure the drying characteristics of G. sylvestre leaves under far-infrared radiation (FIR) and to optimize the specific energy consumption for drying and antioxidant activity of ethanol-water extract of dried leaves. Fresh leaves were harvested and exposed to combinations of four different temperatures (125, 150, 175 and 200 °C) and exposure times (5, 10, 15 and 20 min). Drying kinetics, energy consumption, color changes, total phenolic content (TPC) and antioxidant activities were quantified. Both temperature and drying time have significant (p < 0.05) effects on drying characteristics and antioxidant activity. The equilibrium moisture content was achieved at 200 °C and 18 min. The specific energy decreased and total color changes increased with temperature. Under lower temperatures (125 and 150 °C), TPC and antioxidant activity increased with exposure time, whereas higher exposure time (20 min) with high temperatures (175 and 200 °C) significantly decreased TPC and antioxidant activity. The highest TPC of 30.5 mg TAE/g leaf-fresh weight was achieved at 200 °C and 15 min. The optimal drying conditions achieved from the dissimilarity function method were 200 °C and 8.4 min. Full article

Climate change has drawn the attention not only of scientists but of politicians and societies worldwide. The aim of this paper is to present a method for selecting research studies on climate change, waste management and the role of microbes in the recycling of organic matter in soil that analyze the role of organic agriculture as the main connection between agricultural losses and climate change. VOSviewer version 1.6.18 free software tool was used in this study in order to achieve the bibliometric and mapping approach for studies on the effects of climate change in terms of soil recycled organic matter and microbe interaction. Scopus database (accessed 29 September 2022) indexed a total of 1,245,809 bibliographic items classified into paradigms. The presented documents were downloaded from Scopus as graph-based maps and as distance-based maps in order to reflect the strength of the relation between the items. Climate change includes changes in soil and soil microorganisms as affected by natural climate variations and local weather, which have beneficial or negative effects on soil organic matter. From the examination of the selected papers, it was concluded that climate change and changing precipitation patterns are having an impact on microorganisms, particularly bacterial groups, and thus ecosystem function. Full article

To ensure the care of forests, it is necessary to make them sufficiently accessible by forest roads. The basic working tool are hammers, or round shanks of various shapes, composed of a body and a tip. They are subject to a strong abrasive environment, which often leads to damage up to the complete destruction of the functional part of the tool. For these reasons, it is necessary to deal with the possibilities for increasing their lifetime. One of the possibilities of increasing the service life of these tools is hardfacing by welding. The article deals with the abrasive resistance of the original material of the tool and the hardfacing materials. Based on the chemical analysis of the base material of the tool, we found that the tool is made of manganese steel 38Mn6. This material was used as a standard and was compared with the hardfacing materials Abradur 58, E DUR 600, UTP DUR 600 and OK 84.58. Electron microscopy was used to evaluate the microstructure. Next, the Rockwell hardness measurement was performed on the samples. The original tool material 38Mn6 reached the lowest hardness value, namely, 21 HRC. The highest value was reached by the hardfacing material E DUR 600, namely, 59 HRC. Subsequently, a test of resistance to abrasive wear was performed according to GOST 23.208-79. Based on this test, we can conclude that the highest value of resistance to abrasive wear was achieved by Abradur 58. Even though the hardness of this coating was slightly lower than the hardfacing material E DUR 600, specifically 56 HRC, we can state that this hardfacing material (Abradur 58) achieved the best results among the investigated materials. Full article

The objective of this research is to apply Quality 4.0 (Q4.0) concept in Agriculture 4.0 (A4.0) to digitize the traditional quality management (QM) system and demonstrate the effectiveness of zero-defect manufacturing (ZDM) in the agricultural part manufacturing industry. An autonomous quality management system was developed based on the ZDM system using the Industrial Internet of Things (IIoT). Both traditional and autonomous quality management systems were evaluated using six-sigma quality indicators and machining and inspection cost analysis. The ZDM resulted in a significant improvement in the quality of CARD148 manufacturing, increasing the manufacturing process from a low level of sigma to a high level of sigma (0.75 to 5.10 sigma). The component rejection rate was reduced by a high percentage, leading to significant economic benefits and a significant reduction in machining cost. The process yield was also increased to a high percentage. The developed ZDM was found to be consistent in improving the quality of the turning process, with notable increases in tool life and reduction in inspection cost. The total component cost was reduced significantly, while the PPM value increased notably. While this study focuses on agriculture-related manufacturing organizations, the developed ZDM has potential for other machining industries to improve sigma levels, particularly in industries such as automotive and medical. Full article

In the past few decades, fruits have been increasingly consumed, leading to an increase in global fruit production. However, fresh produce is susceptible to large losses during production and preservation. In the postharvest preservation stage, fruits undergo various technical treatments for maintaining their quality. A widely adopted technology is the application of edible coatings, which can be applied to a diverse range of fruits to regulate the exchange of moisture and gases between the fruit and its environment. In addition, edible coatings provide a significant benefit by allowing the integration of different active ingredients into the coating’s matrix, meaning that these substances will associate with and possibly be eaten together with the fruit. This would help improve the organoleptic and nutritional qualities of the fruit as well as the shelf life. This paper provides an overview of the available data on the typical components used in coating matrix, focusing on the effect of the material combinations and application techniques to fruit properties. The processors can use this knowledge in choosing a suitable coating material and concentration for various fresh and fresh-cut fruits. Additionally, this paper reviews recent developments and limitations in utilizing edible coatings for prolonging the shelf-life of fruits. Full article

Ground speed variations are common and unavoidable during pesticide applications with agricultural sprayers. Field tests were conducted to evaluate the effect of varying ground speeds on spray deposition and quality with a commercial agricultural boom sprayer without a rate controller (CNS) in 2021 and equipped with a rate controller (SRC) in 2022. During each year, the sprayer boom was split evenly among three different nozzle types (XRC, AIXR, and TTI) to attain different droplet sizes (medium, very coarse, and ultra-coarse, respectively). Prior to testing, the sprayer was calibrated to deliver an application rate of 187 L ha⁻¹ at a spray pressure of 207 kPa and ground speed of 9.7 km h⁻¹. For spray deposition and quality assessment, pesticide applications were made at five different ground speeds of 9.7, 12.9, 16.1, 19.3, and 22.5 km h⁻¹, and data were collected by placing water-sensitive paper at different locations across the sprayer boom and in the field. Results for CNS indicated that spray deposition reduced significantly (p < 0.05) with an increase in ground speed across all three nozzle types, primarily due to a decrease in the quantity of spray droplets applied per unit area. The quantity of spray droplets and spray deposition was more consistent among the ground speeds for SRC. Ground speed affected spray quality for both CNS and SRC; however, the spray quality variations were greater for SRC due to an increase in spray pressure with ground speed. Among nozzle types, the trends in spray deposition and quality were similar for the XRC and TTI nozzles as observed for CNS and SRC. However, the AIXR nozzle showed inconsistent spray deposition and quality as ground speed varied. The results of this study indicated agricultural sprayers equipped with a rate controller provide adequate and consistent spray deposition compared to conventional sprayers (no rate controller) when ground speed changes occur during pesticide applications. While spray quality is also affected when using a rate controller, best management practices including proper nozzle selection and application at nominal ground speeds should be followed to minimize these effects and ensure effective technology utilization. Full article

The emergence of precision and digital livestock farming presents an opportunity for sustainable animal farming practices that enhance animal welfare and health. However, this transformation of modern animal farming through digital technology has several implications for the technological, social, economic, and environmental aspects of farming. It is crucial to analyze the ethical considerations associated with the digitalization of modern animal farming, particularly in the context of human–animal relationships and potential objectification. This analysis can help develop frameworks for improving animal welfare and promoting sustainability in animal farming. One of the primary ethical concerns of digital livestock farming is the potential for a digital divide between farmers who have access to advanced technologies and those who do not. This could lead to a disparity in animal welfare and health outcomes for different groups of animals. Additionally, the use of artificial intelligence in digital livestock farming may lead to a loss of personal connection between farmers and animals, which could impact the animal’s well-being. Another ethical concern of digital livestock farming is the potential for the objectification of animals as mere data points. The use of sensors and other monitoring technologies can provide valuable data on animal health and behavior, but it is important to remember that animals are sentient beings with complex emotional and social needs. The use of digital technologies should not lead to neglect of animal welfare or a lack of human responsibility toward animals. Furthermore, social context becomes essential while integrating technologies in livestock farming to overcome ethics. By considering the cultural and societal norms of different communities, we can ensure that the use of digital technologies does not undermine these values. To address these ethical challenges, the development of standards and codes of conduct for the adoption and implementation of digital livestock farming tools and platforms can help ensure that animal welfare and sustainability are prioritized. This can help alleviate the privacy concerns of stakeholders and improve sustainability in animal farming practices. Additionally, the use of virtual and augmented reality technologies can provide a way to enhance human–animal interactions and provide more personalized care to animals, further promoting animal welfare. Full article

One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry. Full article

Understanding the spatial variability of factors that influence crop yield is essential to apply site-specific management. The present study aimed to evaluate apparent soil electrical conductivity (ECa) in two fields (A = rainfeed; B = central-pivot irrigation), based on delimited management zones (MZs). In each MZ, the soil density (Sd) was characterized at two soil depths, and whether the delimitation of MZs, based on the spatial variability of ECa, was able to identify regions of the field with different Sd was assessed. In general, MZs with the highest mean value of ECa also presented the highest mean values of Sd. The highest Sd values were observed in the 0.1–0.2 m layer, regardless of the studied area. Regardless of soil texture, the proposed ECa was able to detect in-field differences in Sd. The delimitation of MZs, based on the spatial variability of ECa mapping, was able to differentiate the mean values of Sd between MZ 1 (1.53 g cm⁻³) and MZ 2 (1.67 g cm⁻³) in field A, in the 0.1–0.2 m layer. A statistical difference was observed for the mean values of Sd, in MZ 1, at layer 0.1–0.2 m, when comparing the two fields: A (1.53 g cm⁻³) and B (1.64 g cm⁻³). We suggest that further studies should be carried out to confirm the efficiency of ECa in detecting the soil bulk density at different soil depths. Full article

Chemical spraying is one of the most important and frequently performed intercultural agricultural operations. It is imperative to select the appropriate spraying technology as a selection of ineffective one leads to the wastage of a considerable volume of applied chemicals to the non-target area. Many precision technologies have been developed in the past few decades, such as image processing based on real-time variable-rate chemical spraying systems, autonomous chemical sprayers using machine vision and nozzle control, and use of unmanned aerial and ground vehicles. Cotton defoliation is a natural physiological process, but untimely and inadequate leaf defoliation by natural process hinders the mechanical cotton harvest. Induced defoliation is practiced by applying defoliants to address the issue with the natural process of defoliation. This paper covers spraying technologies in agriculture, cotton plants, cotton defoliation, new defoliant spraying systems, and the recent field test. The new spraying system attached to an autonomous mobile robot aims to improve the delivery of defoliant chemicals by adding a spray unit on the side of the plant. Preliminary results of the water-sensitive paper test at the field showed adequate penetration with low flow rates. This is a huge development as there is a huge potential to save on the cost of applying defoliant chemicals. Full article

Utilization of agricultural waste can be done by converting it with conventional fuels to energy. For this purpose, it is necessary to understand the properties of waste and its mixture with the fossil fuels important for its storage and conversion. The objective of the work was to examine the influence of moisture content and the composition of agricultural waste with hard coal mixtures on the mechanical and rheological properties of the waste. The materials tested were powdered biomass: dried distillers grains with solubles (DDGS), meat and bone meal (MBM), and hard coal (HC). Mechanical properties were measured to investigate flowability with the Jenike shear tester. A technique with an annular powder rheometer was applied for rheological measurements. It was shown that an increased moisture content worsened the flowability of the mixtures, while an increased biomass content reduced the influence of moisture and stabilized the mechanical properties of the mixtures in quasi-static conditions. In dynamic conditions, moisture decreased the mechanical strength of the mixtures and increased their flowability. Full article

Object detection is one of the most promising research topics currently, whose application in agriculture, however, can be challenged by the difficulty of annotating complex and crowded scenes. This study presents a brief performance assessment of YOLOv7, the state-of-the-art object detector, in comparison to YOLOv4 for apple flower bud classification using datasets with artificially manipulated image annotation qualities from 100% to 5%. Seven YOLOv7 models were developed and compared to corresponding YOLOv4 models in terms of average precisions (APs) of four apple flower bud growth stages and mean APs (mAPs). Based on the same test dataset, YOLOv7 outperformed YOLOv4 for all growth stages at all training image annotation quality levels. A 0.80 mAP was achieved by YOLOv7 with 100% training image annotation quality, meanwhile a 0.63 mAP was achieved with only 5% training image annotation quality. YOLOv7 improved YOLOv4 APs by 1.52% to 166.48% and mAPs by 3.43% to 53.45%, depending on the apple flower bud growth stage and training image annotation quality. Fewer training instances were required by YOLOv7 than YOLOv4 to achieve the same levels of classification accuracies. The most YOLOv7 AP increase was observed in the training instance number range of roughly 0 to 2000. It was concluded that YOLOv7 is undoubtedly a superior apple flower bud classifier than YOLOv4, especially when training image annotation quality is suboptimal. Full article

The canned fruits industry utilizes high amounts of water and energy, which results in the generation of vast quantities of wastewater and solid waste. The main scope of this study was to compare the environmental footprint of a canned fruits industry (alternative scenario) equipped with appropriate processes (pulsed electric fields, anaerobic digestion, composting, membrane bioreactors, and ultraviolet treatment) that sufficiently save energy and valorize production wastes to a typical setup that uses conventional waste methods (conventional scenario) via conducting a life cycle assessment study. Based on the results, the life cycle assessment confirmed the fact that the incorporation of the proposed methods, as described in the alternative scenario, dramatically reduced the environmental footprint of the industry, with certain environmental impact categories reaching a decrease of up to 90.00%. More specifically, according to the obtained results, a decrease of 11.81, 64.56, and 89.79% in regards to climate change, freshwater ecotoxicity, and freshwater consumption, respectively, was achieved in the alternative scenario compared to the conventional method. The study verified the environmental advantages of integrating such energy saving and waste treatment/valorization technologies across the canned fruits industry’s processing chain, contributing to environmental sustainability and safety. Full article

Permanent dryland pastures are the basis of animal feed in extensive grazing systems. Seasonality and inter-annual climatic variability, associated with shallow, acidic, and not very fertile soils, result in low productivity and rapid degradation of pasture quality, which requires the supplementation of animal feed. In this study, carried out in a biodiverse pasture field in the Mediterranean region of southern Portugal, the vegetation index (NDVI, Normalized Difference Vegetation Index) obtained from measurements performed by a proximal optical sensor (PS) and satellite images (RS) was used to assess pasture quality parameters (pasture moisture content, PMC, crude protein, CP, and neutral detergent fiber, NDF). The monitoring was carried out throughout the 2021/2022 pasture growing season. Significant correlations were obtained between the NDVI obtained by PS and RS (R² of 0.84) and the reference values of pasture parameters obtained in laboratory protocols: PMC (R² of 0.88 and 0.78, respectively), CP (R² of 0.67 and 0.63, respectively), and NDF (R² of 0.50 and 0.46, respectively). This case study also demonstrated the spatial and temporal variability of vegetative vigour and, consequently, of pasture quality in the Montado, the characteristic Mediterranean ecosystem. These results show the pertinence of these technologies in supporting the decision-making process of the farm manager, namely, to estimate the supplementation needs of animals in critical phases, especially after the spring production peak and before the autumn production peak. Full article

In present-day Japan, high quality is the first requirement of rice production. To maintain the quality of rice, the prejudgment technique has been proposed to control rice growth or to separately harvest rice depending on its quality. Since the quality of rice is generally indexed by grain protein content, which is strongly affected by nitrogen content of leaves, one of the major prejudgment techniques is based on leaf greenness evaluation (i.e., SPAD value). However, the technique is under research and not popular with the farmers because the reliability of prejudgment is inadequate. In this study, we investigated the leaf SPAD value at different growth stages of different cultivars and with cultivation methods in farmer fields over four years, and we validated the applicability of prejudgment by comparing with the grain protein content. The results showed that the grain protein content was positively correlated with leaf SPAD value at the maturity stage, but correlated weakly with those at the booting, heading, and milking stages. Since the regression coefficients significantly differed depending on the year, cultivar, and planting method, the acquisition of a regression equation for each target is recommended to predict grain protein content more accurately. The validation in this study suggests that the prejudgment of grain protein content just before harvest has generality for several targets and is useful for harvesting rice depending on the quality. The results in this study may contribute to the attempts to evaluate SPAD value and then rice quality by remote sensing. Full article

Colour is a property widely used in many fields to extract information in several ways. In soil science, colour provides information regarding the chemical and physical characteristics of soil, such as genesis, composition, and fertility, amongst others. Thus, accurate estimation of soil colour is essential for many disciplines. To achieve this, experts traditionally rely on comparing Munsell colour charts with soil samples, which is a laborious process. In this study, we proposed using artificial neural networks to catalogue soil colour with a two-step classification. Firstly, the hue variable is estimated, and then the remaining two coordinates, value and chroma. Our experiments were conducted using three different, common cameras (one digital camera and two mobile phones). The results of our tests showed a 20% improvement in classification accuracy using the lowest-quality camera and an average accuracy of over 90%. Full article

In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture. Full article

Gross primary productivity (GPP) is an essential parameter to assess the efficiency of terrestrial ecosystems on carbon transfer. Although GPP is regularly measured with eddy covariance (EC) systems, these are restricted to the tower footprint area, and remote sensing (RS) products have estimated GPP using multispectral vegetation indexes (VIs) from farms to whole ecosystems. Indeed, nowadays, unmanned aerial vehicle (UAV)-based RS technology is becoming more accessible. Accordingly, we propose the estimation of GPP using VIs at high spatial resolutions using UAVs and multi-spectral cameras. A small typical farm in Colombia was cultivated with carrot as our base crop. An EC system was installed to estimate GPP and was used as a reference. A total of 24 VIs from UAV-based RS products were selected and compared with the GPP of the EC system. A cross-validation process was performed, and seven VIs obtained a high R² score (0.76–0.78). The accumulated GPP, estimated with the best index (NIRv) was 520.3 g C m⁻², while the GPP-EC estimate was 580.4 g C m⁻² (10.3% error). This work showed that it is possible to estimate the GPP of carrot crops using UAV-based RS, VIs, and linear regression models, which can be used in further research on GPP using UAVs. Full article

The development of robotic-based agricultural machinery systems has significantly increased in recent years. Many autonomous systems have not yet been measured based on sustainability and economic performances, even though automation is regarded as an opportunity to increase safety, dependability, productivity, and efficiency. The operational aspect, economic viability, and environmental impact of replacing conventional machinery with robotized alternatives are the primary focus of this study. The robot considered in this research is designed for extensive fieldwork, where PTO and external hydraulics are required. This robot is equipped with two 75 (hp) Kubota diesel engines with a total engine gross power of up to 144 (hp). Both robotic system and conventional machinery were described, and different scenarios were used to examine various operational and environmental indicators, as well as individual cost elements, considering various field sizes and working widths of implements used in seeding and weeding operations. The findings demonstrate that the robotic system outperforms conventional machinery in terms of operational efficiency by as much as 9%. However, the effective field capacity comparison reveals that the conventional system has a field capacity that is up to 3.6 times greater than that of the robotic system. Additionally, the total cost per hour of the robotic system is up to 57% lower than that of the conventional system. The robotic system can save up to 63.3% of fuel during operation, resulting in the same percentage reduction in CO₂ emissions as the conventional system, according to a comparison of fuel consumption. Full article

The introduction of computer vision and machine learning into agricultural systems has produced significant new opportunities for high precision application of liquid products in both grain and livestock agriculture. These technologies, which enable liquid application in site-specific, non-broadcast applications, are driving new evaluations of nozzle technologies which apply a consistent dose of liquid product in a non-conventional manner compared to historic perceptions. This field of innovation is driving the need for improved high-capacity systems for evaluating nozzle performance in high-precision applications. Historically, patternator tables with volumetric measurements of total applied liquid have served as the standard for fluid nozzle evaluation. These volumetric measurements are based on measuring the displaced distance of liquid over a defined time to determine flow rate. However, current distance sensors present challenges for achieving small-volume measurements and enabling automation at a scale necessary to meet innovation demands of high-precision nozzle systems. A novel concept for high speed and automated measurement of a high precision patternator table was developed using an ultrasonic sensor and a carefully designed liquid retainment system to maximize measurement precision. The performance of this system was quantified by comparing calibrations and performance across different vessels for volume measurement (tubes and pipes) used in the application of a nozzle patternator. A total of three square tubes (15.9, 22.3, 31.0 mm widths) and three pipes (25.2, 27.0, 35.1 mm diameters) were evaluated, with the 27 mm pipe matching the ultrasonic sensor’s rating. All calibrations were successful, depicting linear characteristics with R² > 0.99. The smallest pipe presented issues for the sensor to measure in post-calibration and was thus not evaluated further. The residual values from operational performance highlight that the 25.2 mm tube and the 27.0 mm pipe are highly accurate with no indication of bias or non-normality. The relative uncertainty ranges from 2.9 to 42% (350 mL to 25 mL) depending on the tube and pipe cross-sectional diameter or width with the sensor accuracy and uncertainty in the tube and pipe area being the largest factors. The results of this study indicate that the 25.2 mm tube and the 27.0 mm pipe could be excellent options for autonomous liquid volume measurement with the ultrasonic sensor. A key challenge identified in this study is that the assumptions in the sensor’s intrinsic calibration are violated with the tubes and pipes evaluated. Full article

Among the most common and serious tomato plant pests, leafminer flies (Liriomyza sativae) are considered one of the major tomato-plant-damaging pests worldwide. Detecting the infestation and quantifying the severity of these pests are essential for reducing their outbreaks through effective management and ensuring successful tomato production. Traditionally, detection and quantification are performed manually in the field. This is time-consuming and leads to inaccurate plant protection management practices owing to the subjectivity of the evaluation process. Therefore, the objective of this study was to develop a machine learning model for the detection and automatic estimation of the severity of tomato leaf symptoms of leafminer fly attacks. The dataset used in the present study comprised images of pest symptoms on tomato leaves acquired under field conditions. Manual annotation was performed to classify the acquired images into three groups: background, tomato leaf, and leaf symptoms from leafminer flies. Three models and four different backbones were compared for a multiclass semantic segmentation task using accuracy, precision, recall, and intersection over union metrics. A comparison of the segmentation results revealed that the U-Net model with the Inceptionv3 backbone achieved the best results. For estimation of symptom severity, the best model was FPN with the ResNet34 and DenseNet121 backbones, which exhibited lower root mean square error values. The computational models used proved promising mainly because of their capacity to automatically segment small objects in images captured in the field under challenging lighting conditions and with complex backgrounds. Full article

Smart agriculture is a concept that refers to a revolution in the agriculture industry that promotes the monitoring of activities necessary to transform agricultural methods to ensure food security in an ever-changing environment. These days, the role of technology is increasing rapidly in every sector. Smart agriculture is one of these sectors, where technology is playing a significant role. The key aim of smart farming is to use the technologies to increase the quality and quantity of agricultural products. IOT and digital image processing are two commonly utilized technologies, which have a wide range of applications in agriculture. IOT is an abbreviation for the Internet of things, i.e., devices to execute different functions. Image processing offers various types of imaging sensors and processing that could lead to numerous kinds of IOT-ready applications. In this work, an integrated application of IOT and digital image processing for weed plant detection is explored using the Weed-ConvNet model to provide a detailed architecture of these technologies in the agriculture domain. Additionally, the regularized Weed-ConvNet is designed for classification with grayscale and color segmented weed images. The accuracy of the Weed-ConvNet model with color segmented weed images is 0.978, which is better than 0.942 of the Weed-ConvNet model with grayscale segmented weed images. Full article

This research investigated the best economically viable power source with the least environmental impact and socially acceptable for the maize crop. Maize is one of the key economic crops in Pakistan. Solar-, electric-, and diesel-powered drip irrigation systems (DIS) were considered for comparative study. We selected 45 sites of maize crop to collect the data, with an area of 1–3 ha, from three divisions. For economic viability, the benefit:cost ratio, life cycle cost, and payback period were calculated, and CO₂ emissions were calculated to assess the environmental impact. The SPSS model was used for one-way ANOVA followed by post hoc and chi-squared tests to check the significance level between all power sources. It was found that the B-C of electric power, solar, and diesel drip irrigation systems was 1.65, 1.52, and 1.44, respectively. Solar, diesel, and electricity power DIS have CO₂ emissions of 0.02, 0.730, and 1.106 tons/ha, respectively. The research concludes that solar power and electric power are the best sources for the environment and economically, respectively. It is recommended that solar power DIS be subsidized, which will help to lower CO₂ emissions and reduce the electricity shortfall in Pakistan. Full article

The use of agricultural tractors is a major concern in agriculture safety due to the high level of risk of loss of stability combined with the frequent absence of passive safety devices such as rollover protective structures (ROPSs). Indeed, although in most cases the ROPS is installed, when working in vineyards, orchards, or in other cases of limited crop height, the tractor is usually equipped with a foldable ROPS (FROPS), which is often misused because the effort needed for raising/lowering is excessive and the locking procedure is time-consuming. Thus, the goal of this research is to investigate the problem from the ergonomics point of view, developing a support system capable of facilitating FROPS operations. The research outcome consists of the development of a retrofitted full assistance system (FAS) for lowering/raising the FROPS by means of electric actuators. Additionally, an automatic locking device (ALD) was also developed to safely and automatically lock the FROPS. Both the FAS and ALD systems were implemented following a reverse-engineering approach, while their final validation was performed by means of a real prototype tested in a laboratory. The results achieved can contribute to expanding knowledge on human-centered research to improve safety in agriculture and thus social issues of sustainable agricultural systems. Full article

Plastic contamination is a burning issue costing the global cotton and textile industries billions of dollars annually. Any time plastics from different sources end up in a cotton lint bale, the value to the textile mills plummets significantly. Various industry players have therefore made a concerted effort to find lasting solutions to the menace posed by plastic to cotton profitability and sustainability. Nevertheless, until now, there have been no up-to-date comprehensive documents detailing the numerous and ever-growing efforts committed to solving this challenge. Therefore, this article provides a detailed yet compact review of this highly dynamic subject matter. First, it puts into perspective plastic contamination in the cotton and textile industries. Then, the cotton value chain is subdivided into phases from pre-cultivation to textile mills. The root causes of plastic contamination are discussed in each stage, followed by discussions of some already developed and emerging solutions in response to the challenge by the affected industries and researchers. Concluding from the author’s perspective, the paper makes projections for the future directions of plastic mitigation efforts within the cotton and textile industries. This article also infers from the reviewed literature that research on finding alternative materials to plastic as module wrap, the development of new, effective, and all-condition plastic sensing techniques for ginning and spinning equipment, and standardized protocols for UAV in-field surveys of plastic trash are some of the areas that will be beneficial to finding a permanent solution to the challenge. Full article

The use of roller crimpers to terminate plants and obtain a natural mulch before cash crop establishment has been identified as a valid and sustainable approach to control weeds. Several enhancements have been evaluated to improve and speed up plant termination to avoid delays in cash crop planting and consequent yield losses, which can occur with standard roller crimpers. In the present study, a new prototype machine provided with a roller crimper and an undercutting blade, allowing it to simultaneously crimp plant stems and cut root systems, has been designed, realized, and tested. The aim of the research was therefore to evaluate the effectiveness of the prototype for plant termination and to compare it with a commercial roller crimper. The termination was performed on a spontaneous vegetation cover (weeds). A monophasic exponential decay model to evaluate the weed termination rate over time was performed. The fitted model showed that the prototype is able to achieve a greater and faster weed devitalization compared to the commercial roller crimper, with a lower plateau (0.23 vs. 5.35 % of greenness of plant material, respectively) and higher constant of decay (1.45 vs. 0.39 day⁻¹, respectively). Further studies are needed to evaluate the prototype’s effectiveness in relation to different soil textures, moisture conditions, and amounts of plant biomass to manage, to further improve the machine and extend its use in a broad range of situations, including cover crop termination. Full article

Whole-plant corn has been previously investigated as a biomass feedstock. Current approaches are analogous to harvesting whole-plant corn for livestock feed or biogas production. They include utilizing a self-propelled forage harvester to harvest the plant as a bulk material and storing it anaerobically. This process leads to grain damage, reducing the marketability of the grain after fractionation. This work investigated a process that included harvesting and anaerobically storing whole-ear corn with corn stover as an alternative. Over two harvest seasons, dry matter losses, moisture content changes, and grain damage were assessed after anaerobic storage. Less than 3% grain damage was observed across all treatments. Stover moisture decreased by 3% to 7% wet basis. Depending on the harvest year (p < 0.001), grain moisture content increased by 7 to 10 percentage points wet basis (p = 0.012). Cob moisture increased by about four percentage points wet basis regardless of harvest year (p = 0.49). Dry matter losses for the overall treatment were less than 3% across both harvest seasons, but high variability was observed when reviewing the losses in the ear and stover fractions. Based on this work, whole ear storage should be considered where subsequent grain fractionation and the marketability of the grain fraction are a concern. Full article