AgriEngineering, Volume 7, Issue 5 (May 2025) – 27 articles

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield reduction due to the temperature and environment. In this study, a lightweight pepper disease identification method, DD-YOLO, based on the YOLOv8n model, is proposed. First, the deformable convolutional module DCNv2 (Deformable ConvNetsv2) and the inverted residual mobile block iRMB (Inverted Residual Mobile Block) are introduced into the C2F module to improve the accuracy of the sampling range and reduce the computational amount. Secondly, the DySample sampling operator (Dynamic Sample) is integrated into the head network to reduce the amount of data and the complexity of computation. Finally, we use Large Separable Kernel Attention (LSKA) to improve the SPPF module (Spatial Pyramid Pooling Fast) to enhance the performance of multi-scale feature fusion. The experimental results show that the accuracy, recall, and average precision of the DD-YOLO model are 91.6%, 88.9%, and 94.4%, respectively. Compared with the base network YOLOv8n, it improves 6.2, 2.3, and 2.8 percentage points, respectively. The model weight is reduced by 22.6%, and the number of floating-point operations per second is improved by 11.1%. This method provides a technical basis for intensive cultivation and management of chili peppers, as well as efficiently and cost-effectively accomplishing the task of identifying chili pepper pests and diseases. Full article

This study presents the development of a system based on convolutional neural networks for the automated detection and counting of Gossypium barbadense fruits, specifically the IPA cotton variety, during its maturation stage, known as “mota”, in crops located in the Lambayeque region of northern Peru. To achieve this, a dataset was created using images captured with a mobile device. After applying data augmentation techniques, the dataset consisted of 2186 images with 70,348 labeled fruits. Five deep learning models were trained: two variants of YOLO version 8 (nano and extra-large), two of YOLO version 11, and one based on the Faster R-CNN architecture. The dataset was split into 70% for training, 15% for validation, and 15% for testing, and all models were trained over 100 epochs with a batch size of 8. The extra-large YOLO models achieved the highest performance, with precision scores of 99.81% and 99.78%, respectively, and strong recall and F1-score values. In contrast, the nano models and Faster R-CNN showed slightly lower effectiveness. Additionally, the best-performing model was integrated into a web application developed in Python, enabling automated fruit counting from field images. The YOLO architecture emerged as an efficient and robust alternative for the automated detection of cotton fruits and stood out for its capability to process images in real time with high precision. Furthermore, its implementation in crop monitoring facilitates production estimation and decision-making in precision agriculture. Full article

This study presents a remote sensing (RS) approach for monitoring invasive aquatic species and assessing their impact on water quality in the Lis Valley Irrigation District (LVID), Portugal. Using high-resolution PlanetScope imagery (3.7 m resolution), this method overcomes spatial limitations in narrow irrigation canals. Representative sub-zones were selected to analyze spatial and temporal trends, and vegetation indices (Normalized Difference Vegetation Index—NDVI, Enhanced Vegetation Index—EVI, Green Chlorophyll Index—GCI) were calculated to map the spread of Eichhornia crassipes (water hyacinth—WH) and Myriophyllum aquaticum (parrot’s feather—PF). All three vegetation indices exhibited significant linear regressions with pH, with the EVI showing the highest coefficient of determination (R² = 0.761), followed by the NDVI (R² = 0.726) and GCI (R² = 0.663), with p-values and ANOVA p-values below 0.05. Dissolved Oxygen (DO) also showed strong correlations, particularly with the GCI (R² = 0.886 for both DO concentration and saturation). The NDVI and EVI demonstrated significant regressions for these parameters, with R² values between 0.661 and 0.862. The results demonstrate the potential of RS to detect invasive species and assess their ecological impact, providing a cost-effective tool for management strategies in irrigation systems. Future research should integrate more field data and extend the study period to enhance classification accuracy. Full article

The characterization of soil attribute variability often requires dense sampling grids, which can be economically unfeasible. A possible solution is to perform targeted sampling based on previously collected data. The objective of this research was to develop a method for mapping soil attributes based on Management Zones (MZs) delineated from Sentinel-1 radar data. Sentinel-1 images were used to create time profiles of six indices based on VV (vertical–vertical) and VH (vertical–horizontal) backscatter in two agricultural fields. MZs were delineated by analyzing indices and VV/VH backscatter bands individually through two approaches: (1) fuzzy k-means clustering directly applied to the indices’ time series and (2) dimensionality reduction using deep-learning autoencoders followed by fuzzy k-means clustering. The best combination of index and MZ delineation approaches was compared with four soil attribute mapping methods: conventional (single composite sample), high-density uniform grid (one sample per hectare), rectangular cells (one composite sample per cell of 5 to 10 hectares), and random cells (one composite sample per cell of varying sizes). Leave-one-out cross-validation evaluated the performance of each sampling method. Results showed that combining the VV/VH index and autoencoders for MZ delineation provided more accurate soil attribute estimates, outperforming the conventional, random cells, and often the rectangular cell method. In conclusion, the proposed methodology presents scalability potential, as it does not require prior calibration and was validated on soil types commonly found across Brazil’s agricultural regions, making it suitable for integration into digital platforms for broader application in precision agriculture. Full article

This study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted in a mean average precision (mAP), precision, and recall of 99.5%, 99.9%, and 100.0%, respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real time through a web dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming. Full article

To address the inefficiency and high cost of manual potato pickup in segmented harvesting, a dual-disc potato pickup and harvesting device was designed. The device utilizes counter-rotating dual discs to gather and preliminarily lift the potato–soil mixture, and combines it with an elevator chain to achieve potato–soil separation and transportation. Based on Hertz’s collision theory, the impact of disc rotational speed on potato damage was analyzed, establishing a maximum speed limit (≤62.56 r/min). Through kinematic analysis, the disc inclination angle (12–24°) and operational parameters were optimized. Through coupled EDEM-RecurDyn simulations and Box–Behnken experimental design, the optimal parameter combination was determined with the potato loss rate and potato damage rate as evaluation indices: disc rotational speed of 50 r/min, disc inclination angle of 16°, and machine forward speed of 0.6 m/s. Field validation tests revealed that the potato loss rate and potato damage rate were 1.53% and 2.45%, respectively, meeting the requirements of the DB64/T 1795-2021 standard. The research findings demonstrate that this device can efficiently replace manual potato picking, providing a reliable solution for the mechanized harvesting of potatoes. Full article

This study employs RGB imagery and machine learning techniques to detect Tagosodes orizicolus infestations in “Tinajones” rice crops during the flowering stage, a critical challenge for agriculture in northern Peru. High-resolution images were acquired using an unmanned aerial vehicle (UAV) and preprocessed by extracting 256 × 256-pixel segments, focusing on three classes: infested zones, non-cultivated areas, and healthy rice crops. A dataset of 1500 images was constructed and utilized to train deep learning models based on VGG16 and ResNet50. Both models exhibited highly comparable performance, with VGG16 attaining a precision of 98.274% and ResNet50 achieving a precision of 98.245%, demonstrating their effectiveness in identifying infestation patterns with high reliability. To automate the analysis of complete UAV-acquired images, a web-based application was developed. This system receives an image, segments it into grids, and preprocesses each section using resizing, normalization, and dimensional adjustments. The pretrained VGG16 model subsequently classifies each segment into one of three categories: infested zone, non-cultivated area, or healthy crop, overlaying the classification results onto the original image to generate an annotated visualization of detected areas. This research contributes to precision agriculture by providing an efficient and scalable computational tool for early infestation detection, thereby supporting timely intervention strategies to mitigate potential crop losses. Full article

Sunflower press cake (SPC), a by-product of the edible oil industry, represents a promising source of plant-based protein. This study aimed to investigate and optimize protein recovery from SPC using conventional (CE) and advanced extraction techniques, including Ultrasound and Microwave-Assisted Extraction (UMAE), Pressurized Liquid Extraction (PLE) and Enzyme-Assisted Extraction (EAE). The protein content both in extracts and in the precipitated mass was measured through Lowry assay, while the amino acid profile of the extracted proteins under optimal conditions was analyzed via High-Performance Liquid Chromatography (HPLC). Extraction parameters were optimized using response surface methodology (RSM) for each method. Among the novel methods studied, UMAE and PLE demonstrated superior efficiency over CE, yielding higher protein recovery in significantly shorter extraction times. Optimal UMAE conditions (10 min, 0.03 g/mL, 450 W microwave power, and 500 W ultrasound power) yielded a precipitation yield (PY) of 21.2%, protein recovery in extract (PR_E) of 79.9%, and protein recovery in precipitated mass (PR_P) of 66.3%, with a protein content (PC_P) of 902.60 mg albumin eq./g. Similarly, optimal PLE conditions (6 min, 0.03 g/mL, and 50 °C) resulted in PY, PR_E, and PR_P of 17.7, 68.9, and 47.4%, respectively, with a PC_P of 932.45 mg albumin eq./g. EAE using Aspergillus saitoi protease was comparatively less effective. The amino acid profiling confirmed SPC as a valuable protein source, with glutamic acid, arginine, and aspartic acid being the most abundant. These results highlight the potential of UMAE and PLE as efficient strategies for valorizing edible oil industry by-products into high-quality protein ingredients for food and biotechnological applications. Full article

Precision agriculture and advanced sowing technologies, including variable sowing rates, can be used to optimise sunflower yields by ensuring a uniform plant distribution, efficient resource utilisation, and adaptation to soil variability. These agronomic and technological innovations help mitigate field heterogeneity effects, enhancing sunflower establishment, growth, and overall yield stability. The main goal of this research was to analyse the interactions among management, soil, and environmental variables and their effects on the sowing quality and yield in the case of precision sunflower production. A sowing field experiment was set up in the period between 2021 and 2023 to identify these effects and their complex interactions, which were evaluated with the aim of improving the sowing and yield parameters, while also understanding the importance of each different parameter. As a key outcome for precision sowing, this research demonstrates that the variability in sowing parameters—such as double and missing sowing rates, as well as sowing uniformity—was significantly influenced by the field conditions, productivity zones, and nominal crop density. These findings underscore the importance of implementing site-specific management strategies to optimise sunflower production and maximise yields. Overall, of the various factors influencing sunflower production, the crop year proved to be more significant than the soil parameters due to the strong influence of annual climatic variability. The field zone was also identified as a more critical determinant of sowing and yield variability than crop density, highlighting the importance of spatial management within fields, and also marking possible directions for future research. Full article

Armillaria root rot (ARR) is a fungal disease caused by Desarmillaria caespitosa and the leading cause of peach tree decline in the Southeastern U.S. It affects the roots and lower stems of trees, leading to the decay of the tree’s root system. Planting peach trees shallow on berms and excavating soil around the root collar after two years can extend the economic life of infected trees. However, berms pose operational challenges, including elevation changes, soil erosion from water flow, and herbicide and fertilizer runoff, thereby reducing orchard management efficiency. This study aimed to develop a tractor-mounted rotary tillage method to flatten the area between peach trees planted on berms, improving safety and reducing runoff. A custom paddle wheel attachment (20.3 cm height, 30.5 cm length) was retrofitted to an existing mechanical orchard weed management implement equipped with a hydraulic rotary head. A hydraulic flow meter, two pressure transducers, and an RTK-GPS receiver were integrated with a wireless data acquisition system to monitor the paddle wheel rotational speed and tractor ground speed during field trials. The effects of three paddle wheel speeds (132, 177, and 204 RPM) and three tractor ground speeds (1.65, 2.255, and 3.08 km/h) were evaluated in two orchards with Cecil sandy loam soil (bulk density: 1.93 g/cm³; slope: 2–6%). The paddle wheel speed had a greater influence on the torque and power requirements than the tractor ground speed. The combination of a 177 RPM paddle speed and 3 km/h tractor speed resulted in the smoothest soil surface with minimum torque demand, indicating this setting as optimal for flattening berms in similar soil conditions. Future research will include optimizing the paddle wheel structure and equipping the berm leveling machine with tree detection sensors to control the rotary head position. Full article

While conventional sericulture has developed effective disinfection methods, the increasing demand for silk and pupae is driving mechanization, potentially altering or introducing silkworm pathogens. New disinfection strategies are essential for sustainable sericulture production. This study first investigated the bacterial community differences between conventional and mechanized silkworm-rearing environments. Then, under the mechanized environment, we evaluated three commercially available disinfectants with different mechanisms: hypochlorous acid (HClO), nano platinum-polyhexamethylene guanide (Pt-PHMG), and medium-chain fatty acids (MCFA). Our results indicated significant bacterial differences between the two environments, with potential pathogenic bacteria present in both environments. Moreover, the bacterial communities remained relatively stable, while conventional disinfection methods were less effective in mechanized conditions. In contrast, regardless of whether they were applied before or after silkworm rearing, all three disinfectants demonstrated significant efficacy, with the total environmental bacterial load reduced by approximately 0.5 to 1 order of magnitude after application. Among them, Pt-PHMG exhibited the best performance by inhibiting pathogens such as Staphylococcus, Enterococcus, and Bacillus, followed by MCFA and HClO. The results also suggested a need for stronger disinfection strategies after silkworm rearing. These findings not only provide important hygiene practices to ensure mechanized silkworm rearing, but also offer valuable insights for the future development of disinfection strategies in modern sericulture. Full article

The integration of machine learning (ML) into self-governing spraying systems is one of the major developments in digital precision agriculture that is significantly improving resource efficiency, sustainability, and production. This study looks at current advances in machine learning applications for automated spraying in agricultural mechanisation, emphasising the new innovations, difficulties, and prospects. This study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics, sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised, and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection, as well as accurate application of agricultural plant protection. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations’ efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advances, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods. Full article

Measuring the weight of broilers is one of the most important yet labor-intensive metrics to monitor throughout a flock’s development. This study aimed to comparatively assess two broiler weighing systems in a commercial production system: an automatic weighing system using a suspended platform, and a manual weighing system. Six flocks, comprising 25,000 birds each, were monitored weekly, and the weight results obtained by manual and automatic methods were compared. Up to the third week of this study, the birds were restricted to the central region of the shed, where the broiler coop was located. From the fourth week onwards, the birds were distributed into four sectors within the shed, divided by fences. Differences in weight were found between the regions of the sheds for the automatic weighing, which demonstrates that the use of an automatic scale for each division is necessary. For the manual weighing, the differences were only found in the last week of rearing, suggesting that throughout the cycle, the weighings could be performed in a single quadrant, representing the shed. Regarding the weighing method, there were statistical differences between manual and automatic weighing. The average values for automatic weighing were 1% lower than the average values for manual weighing. However, from a commercial point of view, this small difference between the methods does not impact the poultry industry. The rational use of automatic scales is recommended to optimize the monitoring of broiler chicken performance, reduce excessive handling and, consequently, minimize animal stress, promoting greater well-being. Full article

Precision livestock farming technologies support health monitoring on farms, yet few studies have evaluated their effectiveness under field conditions using reliable gold standards. This study evaluated a commercially available technology for detecting cough sounds in pigs on a commercial farm. Audio was recorded over six days using 16 microphones across two pig barns. A total of 1110 cough sounds were labelled by an on-site observer using a cough induction methodology, and 8938 other sounds from farm recordings and open-source datasets (ESC-50, UrbanSound8K, and AudioSet) were labelled. A hybrid deep learning model combining Convolutional Neural Networks and Recurrent Neural Networks was trained and evaluated using these labels. A total of 34 audio features were extracted from 1 s segments, including validated descriptors (e.g., MFCC), unverified external features, and proprietary features. Features were evaluated through 10-fold cross-validation based on classification performance and runtime, resulting in eight final features. The final model showed high performance (recall = 98.6%, specificity = 99.7%, precision = 98.8%, accuracy = 99.6%, F1-score = 98.6%). The technology tested was shown to be efficient for monitoring cough sounds in a commercial swine production facility. It is recommended to test the technology in other environments to evaluate the effectiveness in different farm settings. Full article

Plant protection products are necessary to guarantee food security, but their drift into the environment, usually in the form of aerosol, poses a threat to the health of bystanders and surrounding ecosystems. Appropriate testing of plant protection equipment and of its possible configurations is key to reducing drift while guaranteeing treatment efficacy. A key role in drift generation and treatment quality is played by the drop size distribution (DSD) of the employed spray nozzles. The DSD of nozzles can and should be tested before being employed by various methods. This paper recounts the recent experience in testing the DSD generated by two types of agricultural nozzles by an Oxford Lasers N60V Particle/Droplet Image Analysis (PDIA) system. The analyses put in place aimed at identifying the optimal instrument settings and adapting the methodology to the relevant ISO 25358:2018 standard. The cumulated DSD of the two nozzle types have then been fitted with a logistic function, with the aim to obtain nozzle performance models. The fitting has proven highly reliable, with correlation coefficients $R^2\ge 0.98$. These models are a satisfactory starting point to compare the performance of different PPEs. In perspective, the fitted nozzle models can help bridge the mathematical gap with other aspects of PPE performance, such as aerosol generation and downwind transport. Full article

This study was based on the hypothesis that the hybrid type and its physical–mechanical properties significantly influence the operational efficiency of transplanting systems. Understanding these properties is essential for optimizing the performance of semi-automatic and automatic transplanters. To test this hypothesis, a completely randomized design was implemented to evaluate the physical–mechanical properties of tomato seedlings. A total of 1350 seedlings from three F1 hybrids—Natalie (H1), CID (H2), and Gavilán (H3)—cultivated in central Mexico, were analyzed. The statistical analyses included mean comparisons using Tukey’s test and multiple linear regression to estimate the center of mass (CM). The results indicate that H2 was notable for its total height (${\mathrm{h}}_{\mathrm{t}}$ = 311.76 mm), canopy development in X, Y, and Z axes (170.24 mm, 106.84 mm, and 98.14 mm, respectively), stem diameter (${\mathrm{d}}_{\mathrm{s}}$ = 3.65 mm), total weight (${\mathrm{w}}_{\mathrm{t}}$ = 11.92 g), ${\mathrm{d}}_{\mathrm{e}}$ (78.36 mm) and ${\mathrm{d}}_{\mathrm{p}}$ (233.40 mm) distances, and oscillation period (T = 0.88 s). H1 had the highest stem height (${\mathrm{h}}_{\mathrm{s}}$ = 53.18 mm), ${\mathrm{w}}_{\mathrm{t}}$ = 11.76 g, and root ball (RB) moisture content (MC) (77.36%). H3 had the largest ${\mathrm{d}}_{\mathrm{s}}$ = 3.70 mm, as well as the highest MC in the stem (94.51%) and the remaining foliage (92.92%). Regarding mechanical properties, the average adhesion force (AF) was 4.606 N (H1), 7.470 N (H2), and 3.815 N (H3). The average root ball punching force (RBPF) was 0.36, 0.48, and 0.25 N, respectively. The lowest static friction coefficient (SFC) on a galvanized steel sheet was 0.936. The drop test (DT) revealed an average residual substrate mass of 0.148 g at a height of 500 mm. It can be concluded that the interaction between hybrid type, transplanting age, and MC plays a critical role in the efficient design of semi-automatic and automatic transplanting equipment. This interaction enables process optimization, ensures operational quality, reduces seedling damage, and ultimately enhances and increases the long-term profitability and sustainability of the equipment. Full article

The agricultural sector is evolving with the adoption of smart farming technologies, where Digital Twins (DTs) offer new possibilities for real-time monitoring, simulation, and decision-making. While previous research has explored the Internet of Things (IoT), UAVs, machine learning (ML), and remote sensing (RS) in enhancing agricultural efficiency, a systematic approach to integrating these technologies within a DTs ecosystem remains underdeveloped. This paper presents a systematic review of 167 studies published between 2018 and 2025. The objective of this study is to examine recent advancements in DTs-enabled precision agriculture and propose a comprehensive framework for designing, integrating, and optimizing DTs in smart farming. The study systematically examines the current state of DT adoption, identifies key barriers, and computational efficiency challenges, and provides a step-by-step methodology for DT implementation. The review sheds light on potential future research direction and implications for policy, with the aim to speed up the adoption of DTs-based farm management systems in their operational success and commercial viability through analysis of practical applications and future perspectives. This study presents an innovative strategy for integrating digital and physical systems into agriculture and is an important contribution to existing literature. Full article

The construction of an anaerobic digester coupled with post-digestion low-level aeration for agricultural wastewater treatment is described. The digester employs underwater speakers to accelerate the anaerobic digestion process while retaining solids to reduce the strength of the effluent. The effluent is sent to a holding tank and fed at a low flow rate to an aeration tank to effect partial nitrification of the wastewater. The outlet of this tank is sent to a settling tank to retain biomass that developed in the aeration tank, and the effluent is sent to a small constructed wetland to further reduce wastewater nitrogen and phosphorus. The wetland was planted with the broadleaf cattail, Typha latifolia, and hence led to the formation of a retention basin. The system has reduced energy consumption due to the use of underwater sonic treatment and low-level aeration that is not designed to achieve full nitrification/denitrification but rather to achieve a mixture of ammonium, nitrite, and nitrate that might foster the development of a consortium of organisms (i.e., nitrifiers and Anammox bacteria) that can remediate wastewater ammonium at low cost. The system is meant to serve as a complex where various technologies and practices can be evaluated to improve the treatment of agricultural wastewater. Preliminary data from the system are presented. Full article

To address the challenges of manual apple harvesting and the limitations of existing devices—such as constrained workspace, low efficiency, and limited flexibility—a two-stage, sub-region harvesting device was developed. The design, informed by the fruit distribution characteristics in densely planted dwarf apple orchards, integrates a positioning mechanism and a fruit-picking mechanism, enabling multiple pickings within a single positioning operation to enhance workspace coverage. A forward kinematics model was established using the Denavit–Hartenberg (D–H) parameter method. An improved Monte Carlo simulation based on a hybrid Beta distribution estimated the maximum reachable distances of the fruit-picking reference point in the X, Y, and Z directions as 2146 mm, 2169 mm, and 2165 mm, respectively—adequately covering the target harvesting domain. Incorporating a translational axis structure further expanded the harvesting volume by 1.165 m³, a 42.40% improvement over the conventional 3R configuration. To support adaptive control, a random point–geometry fusion method was proposed to solve for joint variables based on harvesting postures, and an automatic fruit-picking control system was implemented. Experimental validation, including reference point tracking and harvesting tests, demonstrated maximum positioning errors of 1.5 mm and 2.2 mm, a fruit-picking success rate of 76.53%, and an average picking time of 7.24 s per fruit—marking a 4.6% improvement compared to existing devices reported in previous studies. This study provides a comprehensive technical framework and practical reference for advancing mechanized apple harvesting. Full article

The scarcity of information on agricultural development in the semi-arid region of Alagoas limits the spatial understanding of this activity. Government data are generally numerical and lack spatial detail. Remote sensing emerges as an efficient alternative, providing accessible visualization of agricultural areas. This study evaluates the performance of MapBiomas in monitoring agricultural areas in the semi-arid region of Alagoas, comparing it to a Random Forest model adjusted for the region using higher-resolution images. The first methodology is based on land use and land cover (LULC) data from MapBiomas, an initiative that provides information on land use and land cover in Brazil. The second method employs the Random Forest model, calibrated for the region’s dry season, addressing cloud cover issues and allowing for the identification of irrigated agriculture. LULC data were subjected to a precision analysis using 200 points generated within the study areas, extracting LULC information for each coordinate. These points were overlaid on high-resolution images to assess model accuracy. Additionally, field visits were conducted to validate the identification of agriculture. The irrigated area data from the Random Forest model were correlated with irrigation records from SEMARH. MapBiomas presented a Kappa index of 0.74, with precision exceeding 90% for classes such as forest, natural pasture, non-vegetated area, and water bodies. However, the agriculture class obtained an F1 score of 0.56. The Random Forest model achieved a Kappa index of 0.82, with an F1 score of 0.79 for agriculture. The correlation between the total annual irrigated area data from Random Forest and SEMARH records was high (R = 0.85). The Random Forest model yielded better results in classifying agriculture in the semi-arid region of Alagoas compared to MapBiomas. However, classification limitations were observed in lowland areas due to spectral confusion caused by soil moisture accumulation. Full article

Broiler gait score (GS) evaluation relies on manual assessments by experts, which can be laborious, hindering timely welfare management. Deep learning (DL) models, conversely, may serve as a cost-effective solution in evaluating GS via automated detection of broiler mobility. This study aimed to develop a vision-based YOLOv8 model to detect the locations of individual broilers, allowing for continuous tracking of birds within a pen and determining bird walking distances, speeds, idleness and movement ratios, and time at the feeder and drinker ratios. Then, Machine Learning (ML) models were developed to estimate the GS level from the mobility indicators in a lab setting. Ten broilers were color-coded and recorded via a top-view camera for 41 days. Their GS were assessed manually twice per week. The YOLOv8 model was trained, validated, and tested with 600, 150, and 50 images, respectively, and subsequently applied to the dataset yielding each broiler’s mobility indicators. The GS levels and mobility indicators were correlated through Ordinal Logistics (OL), Random Forest (RF), and Support Vector Machine (SVM) ML models. The YOLOv8 model was developed with 91% training, 89% testing, and 87% validation mean average precision (mAP) accuracies in identifying color-coded broilers. After tracking, the model estimated an average of 472.26 ± 234.18 cm hourly distance traveled and 0.13 ± 0.07 cm/s speed by a broiler. It was found that with deteriorated GS levels (i.e., worse walking ability), broilers walked shorter distances (p = 0.001), had lower speeds (p = 0.001), were increasingly idle and less mobile, and were increasingly stationed near or around the feeder. The movement ratio, average hourly walking distance, hourly average speed, and age variables were found to be the most significant variables (p < 0.005) in predicting GS levels. These variables were further reduced to one, the average hourly walking distance, because of high collinearity and were used to predict the GS with ML models. The RF model, outperforming others, was able to predict GS with a generalized R² of 0.62, root mean squared error (RMSE) of 0.54, and 65% classification accuracy. Full article

This study investigates the production of biomass briquettes using waste coconut shell charcoal and cinnamon sawdust, bound by eco-friendly, non-edible binders: cassava peel starch, giant taro starch, and pine resin. The production process involved carbonization of coconut shells, followed by crushing, blending with sawdust, pressing, and a 12-day sun-drying period. The briquettes were tested for calorific value, density, compressive strength, and shatter resistance. The calorific values ranged from 26.07–31.60 MJ/kg, meeting the industrial standards, while densities varied between 0.83 g/cm³ and 1.14 g/cm³, ensuring compactness and efficient combustion. Among the binders, cassava peel starch provided the best bonding strength, resulting in high-density briquettes with superior durability and energy release, showing a calorific value and compressive strength of 2.11 MPa. Giant taro starch also improved durability, though with slightly lower calorific values but better bonding than pine resin. Pine resin, while contributing to high calorific values, reduced compressive strength with increased resin content, making it less suitable for high mechanical strength applications. Proximate analysis revealed that cassava peel starch-based briquettes had moisture content from 6.5% to 8.6%, volatile matter from 15.2% to 23.5%, ash content from 2.1% to 3.2%, and fixed carbon between 69% and 76.2%. Giant taro starch-based briquettes exhibited 63.2% to 75% fixed carbon, while pine resin-based briquettes had the highest fixed carbon content (66.4% to 78.3%), demonstrating the potential of non-edible adhesives for sustainable, high-performance fuel production. Full article

A Codonopsis pilosula film-laying and outcrop transplantation machine is developed to solve problems, such as unstable quality of transplanted seedlings, high intensity of manual work, and low efficiency of work in the seedling transplantation of Codonopsis pilosula. This paper outlines the structure and working principle of the machine and analyzes the key components of the prototype, designs the seed bed preparer, analyzes its working process and the force required for furrowing into the soil. Additionally, based on EDEM discrete element simulation technology, a soil-component simulation model was established. In addition, the Hertz–Mindlin model was selected as the contact model between the discrete element simulation soil particles and the seed bed preparer to simulate the operation process of the seed bed preparer. The structure and relevant parameters of the seedling planting device and soil covering device are determined, the transmission system scheme is established, and the working mechanism of the core components is analyzed. Field experiment results indicate that at forward speeds of 0.20, 0.25, and 0.3 m/s, the average qualified rate of planting depth is 91.08%, and the average qualified rate of plant spacing is 89.8%. The field performance test indicators met national and industry standards, which require both qualified rates to exceed 80%, and the test results met the design requirements, demonstrating the integrated operation of trenching, seedling planting, film-laying, and topsoil covering. Full article

Tomato is a crop with high potential to be used in various food industry co-products, such as sauces. In addition to increasing the supply of differentiated products, processed foods have improved shelf life. However, as a consequence of thermal processing, there may be some important nutritional losses. In this context, the choice of suitable varieties for each type of processing based on the assessment of food losses is extremely important to both the processing industry and the consumer. Therefore, this work aimed to predict the percentage of concentration loss in tomatoes during cooking for sauce production using an artificial neural network (ANN). The prediction was made by analyzing the fresh fruit and comparing it to the cooked product. The study investigated bioactive compounds (vitamin C, ascorbic acid, phenolic compounds, flavonoids, carotenoids, anthocyanins, lycopene, and β-carotene), antioxidant activity (DPPH and FRAP), soluble solids, pH, titratable acidity, ratio, and total sugar. Nine commercial and non-commercial tomato varieties were evaluated. The artificial neural network used was the multilayer perceptron, and its results were compared with first-, second-, and third-degree polynomial regression techniques, evidencing its superiority. This superiority was confirmed by the higher correlation achieved using the ANN (R² = 0.9025), outperforming the first-, second-, and third-degree regressions (R² = 0.8817, 0.8819, and 0.8941, respectively). Furthermore, the ANN achieved a lower mean squared error (MSE = 0.000999) and strong validation performance, reinforcing its greater precision and reliability compared to traditional models. Full article

Aquaponics integrates aquaculture and hydroponics, promoting circularity through the recirculation of water and nutrients. However, waste management remains a challenge. This study aimed to evaluate the anaerobic digestion (AD) of aquaponic effluent (AE) combined with cattle manure (CM) for biogas production. An Indian model biodigester was fed with AE, CM and 1:1, 1:3, and 3:1 W (Water):CM, under anaerobic mono-digestion (MoAD) and 1:1, 1:3, and 3:1 AE:CM under anaerobic co-digestion (CoAD) conditions. The chemical characteristics of the substrates and digestates were assessed, as well as the potential for biogas production over 19 weeks. Overall, CoAD provided better results regarding the chemical characterization of the substrates aimed at biogas production. Notably, the 1:3 AE:CM ratio resulted in the most promising outcomes among the tested conditions. This ratio demonstrated higher efficiency, initiating biogas production by the third week and reaching the highest accumulated volume. It is probable that AE increased the dissolved organic load, optimizing the conversion of organic matter and eliminating the need for additional water in the process. Thus, the CoAD of AE and CM emerged as a promising alternative for waste valorization in aquaponics, contributing to renewable energy generation, agricultural sustainability, and the promotion of the circular economy. Full article

The integration of a centrifugal extraction and pipeline transportation system to improve efficiency in shrimp harvesting management into closed-pond aquaculture systems represents a significant advancement in aquaculture technology. This study introduces and assesses the efficiency of the shrimp harvester compared to manual harvesting methods, examining key parameters such as shrimp harvester quality, shrimp harvester loss rates, and shrimp harvester speed. A particularly noteworthy aspect is the innovative transportation of shrimp through a pipeline, which enhances the transformative potential of this technology. Results indicate that the centrifugal shrimp harvester outperforms manual methods, achieving an impressive yield rate of 3338 kg/h with a minimal loss rate of 0.01%; the trend values for harvester capacity ranged from 0.501 to 1.884 tons of shrimp per hour at 240 rpm, 2.391 to 3.081 tons per hour at 270 rpm, and 3.338 to 3.816 tons per hour at 300 rpm. While this technology shows promise for increasing productivity and minimizing shrimp damage, further investigation is needed to evaluate its economic viability, including operational costs and labor expenses. The study highlights the transformative potential of the centrifugal shrimp harvester and emphasizes the need for ongoing research to ensure its practical application in real-world aquaculture settings. Overall, the centrifugal shrimp harvester is poised to revolutionize shrimp harvesting practices, contributing to more sustainable and efficient aquaculture production. Full article

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt. Full article