AgriEngineering, Volume 7, Issue 6 (June 2025) – 24 articles

Inconsistencies in product weight during fertilizer bagging can lead to material losses and reduced operational efficiency. This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). The dataset used consisted of nine numeric sensor variables. Among the models, RFR achieved the highest predictive accuracy (R² = 0.9638, RMSE = 0.0496, MAE = 0.0338). Feature importance analysis identified the clamping time and air pressure as the most influential variables. A Smart Bagging System was developed using the RFR model, integrating real-time monitoring and automated parameter adjustment. The simulation results show that the system can reduce overweight losses by up to 95%, with potential annual savings of approximately IDR 29 billion. While promising, these results are based on controlled conditions and a limited dataset; further field validation is recommended. The proposed system demonstrates the potential of machine learning to support cost-efficient, real-time process control in industrial bagging operations. This work aligns with SDG 9 and SDG 12 by promoting industrial innovation and reducing resource waste. Full article

Pattern recognition in agricultural soils using interdigitated microwave sensors combined with principal component analysis offers a novel approach to soil characterization. In this study, soil samples were collected at the “El Potrillo” ranch, Chihuahua, Mexico, following extraction and preparation protocols. The results of the PCA of the soils revealed that the first two principal components (PC1 and PC2) explain 99.99% of the variability, with the first principal component accounting for 99.73% of the total variability, allowing for effective discrimination of the samples. A high correlation was observed between the behavior patterns of the deeper samples in the soil and the reference solutions with a lower glyphosate concentration. On the other hand, the samples from the soil surface showed greater similarity to deionized and distilled water. Furthermore, when evaluating interdigitated sensor configurations, it was determined that the 3F sensor is redundant and can therefore be excluded. These findings highlight the effectiveness of the combined use of microwave sensors and PCA to identify patterns in agricultural soils. Full article

Vision transformers (ViTs) have recently gained traction in plant disease classification due to their strong performance in visual recognition tasks. However, their application to tomato leaf disease recognition remains challenged by two factors, namely the need for models that can generalise across diverse disease conditions and the absence of a unified framework for systematic comparison. Existing ViT-based approaches often yield inconsistent results across datasets and disease types, limiting their reliability and practical deployment. To address these limitations, this study proposes the ViT-Based Robust Framework (ViT-RoT), a novel benchmarking framework designed to systematically evaluate the performance of various ViT architectures in tomato leaf disease classification. The framework facilitates the systematic classification of state-of-the-art ViT variants into high-, moderate-, and low-performing groups for tomato leaf disease recognition. A thorough empirical analysis is conducted using one publicly available benchmark dataset, assessed through standard evaluation metrics. Results demonstrate that the ConvNeXt-Small and Swin-Small models consistently achieve superior accuracy and robustness across all datasets. Beyond identifying the most effective ViT variant, the study highlights critical considerations for designing ViT-based models that are not only accurate but also efficient and adaptable to real-world agricultural applications. This study contributes a structured foundation for future research and development in vision-based plant disease diagnosis. Full article

Rural domestic waste slag is often used to prepare organic fertilizer, thereby improving the environment and saving resources. The mixing of the raw materials and fermentation bacteria is key to the preparation of organic fertilizers. In the organic fertilizer continuous conveying device designed in this study, a paddle was substituted for a screw blade for transporting the material to improve the mixing performance. A discrete element method (DEM) model was established for the device. The influences of the paddle rotational speed n and paddle angle α were studied. The simulation results showed that mixing performance was improved when the paddle angle α was 45° and the paddle rotational speed n was 75 rpm, with an RSD of 15.96%. The larger the paddle rotational speed n, the larger the average normal contact force, and the smaller the influence of the paddle angle α. In addition, the paddle rotational speed n and paddle angle α could affect the speed of the particles in all directions in the device. The trajectory of a single particle in the device was analyzed, and it was found that changing the paddle parameters could improve the path length and improve the mixing performance. The research results lay the foundation for designing reasonable paddle parameters. Full article

This article considers the study of the grinding and homogeneity of a feed mixture in a device that combines the processes of grinding and mixing. It was found that it is important to improve the working elements with the elimination of passive zones. In this regard, the purpose of this study is to improve the working elements of the feed preparation device with an assessment of the quality of the grinding and homogeneity of the feed mixture. For the efficiency of grinding, serrated surfaces have been developed along four planes of the hammer, which maximizes the use of the working surfaces of the hammer and eliminates passive zones. The design parameters of the serrated surfaces are the step between the tops of adjacent serrations (t, mm), the height of the serrations (h, mm), the angle of inclination (α, °) and the sharpness of the serrations (o_z, °). It was found that it is necessary to strive to reduce the step between the tops of adjacent serrations t. The results of the experiments with four-sided serrated hammers showed that a significant portion of the crushed grain waste particles was smaller than 1 mm (25.36–34.34%); the particle size was over 1 mm and less than 2 mm (35.09–44.22%); the particle size was over 2 mm and less than 3.55 mm (27.59–28.73%), and an insignificant portion of particles was larger than 3.55 mm (0.99–2.98%). The experiments yielded the following results on the homogeneity of the mixing of grain waste and the control component: 86.6% (after 2 min), 87.2% (after 4 min) and 87.6% (after 6 min). The feed preparation device with the developed four-sided serrated hammers and impact-mixing mechanisms can produce sufficiently crushed and uniformly mixed feed mass. Full article

Powdery mildew is one of the most common diseases affecting strawberry yield and quality. Accurate and timely detection is essential to reduce pesticide usage and labor costs. However, recognizing strawberry powdery mildew in complex field environments remains a significant challenge. In this study, an HSV-based image segmentation method was employed to enhance the extraction of disease regions from complex backgrounds. A total of 14 widely used deep learning models—including SqueezeNet, GoogLeNet, ResNet-50, AlexNet, and others—were systematically evaluated for their classification performance. To address sample imbalance, data augmentation was applied to 2372 healthy and 553 diseased leaf images, resulting in 11,860 training samples. Experimental results showed that InceptionV4, DenseNet-121, and ResNet-50 achieved superior performance across metrics such as accuracy, F1-score, recall, and loss. Models such as MobileNetV2, AlexNet, VGG-16, and InceptionV3 demonstrated certain strengths, and models like SqueezeNet, VGG-19, EfficientNet, and even ResNet-50 showed room for further improvement in performance. These findings demonstrate that CNN models originally developed for other crop diseases can be effectively adapted to detect strawberry powdery mildew under complex conditions. Future work will focus on enhancing model robustness and deploying the system for real-time field monitoring. Full article

With the increasing consumer demand for healthy and natural foods, strawberries have emerged as one of the most popular small berries globally. Consequently, careful investigation of the relationship between leaf photosynthetic activity (source strength) and fruit development (sink strength) during strawberry growth provides important insights for maximizing the production potential of this crop. This objective necessitates accurate strawberry organ segmentation. Recently, advancements in deep learning (DL) have driven the development of numerous semantic segmentation models that have performed effectively on benchmark datasets. Nevertheless, their small-organ plant segmentation efficacy remains insufficiently explored. Consequently, this study evaluates eight representative point-based semantic segmentation models for the strawberry organ segmentation task: PointNet++, PointMetaBase, Point Transformer V2, Swin3D, KPConv, RandLA-Net, PointCNN, and Sparse UNet. The employed dataset comprises two components: the open-source LAST-Straw strawberry dataset and a custom Japanese strawberry dataset. Strawberry point cloud organs were categorized into four classes: leaf, stem, flower, and berry. The sparse convolution-based Sparse UNet achieved the highest mean intersection over union of 81.3, followed by the PointMetaBase model at 80.7. This study provides insights into the strengths and limitations of existing architectures, assisting researchers and practitioners in selecting appropriate models for strawberry organ segmentation tasks. Full article

The acute shortage of agricultural labor necessitates the development of predictive models to optimize farm operations. This study investigates the effectiveness of artificial-intelligence-driven models to accurately forecast the timing and yield of persimmon harvests, using meteorological data alongside historical harvest records. An artificial neural network was designed to estimate peak harvest dates by analyzing key meteorological variables. The model was trained and validated using data from the JA Nara Prefecture Nishiyoshino Sorting Facility and Nara Prefecture Agriculture Research and Development Center. Its reliability was confirmed based on mean absolute error, demonstrating the ability to make predictions with an accuracy of approximately three days. Additionally, extreme gradient boosting models were developed to predict yields, incorporating elevation data to refine predictions at the field scale. The model was trained and validated using data from fields cultivated in the Gojo-Yoshino region. The effectiveness of these models was evaluated using root mean square error, demonstrating an improvement in prediction accuracy of up to 20% with the inclusion of elevation data, illustrating their capability to effectively capture yield variations across different orchards. These models can significantly improve labor management, harvest scheduling, and overall productivity within the realm of smart agriculture. Full article

Threshing and cleaning are crucial for efficient harvest procedures that are carried out to separate the grains from the biomass and eliminate any potential contaminants or foreign debris. This study examines the cleaning capabilities of the grain cleaning equipment Kimseed Cleaner MK3, a vibratory sieve and air-screen device, for tiny oilseed crops, particularly kalonji (Nigella sativa) and sesame (Sesamum indicum L.), which are valued for their industrial, medicinal, and nutritional properties. These crops frequently provide post-harvest cleaning issues because of their tiny size and vulnerability to contamination from weed seeds, plant residues, and immature or damaged conditions. In order to determine the ideal operating parameters, 0.5 kg of threshed seed samples with 10% moisture content were utilised in the experiment. A variety of shaker frequencies (0.1–10 Hz) and airflow speeds (0.1–10 m/s) were assessed. A two-stage cleaning method was applied for sesame: the first stage targeted larger contaminants (6.5–7.0 Hz and 1.25–1.5 m/s), while the second stage targeted finer impurities (5.25–5.5 Hz and 1.75–2.0 m/s). With a single-stage procedure (5.5–6.0 Hz and 1.0–1.5 m/s), kalonji was successfully cleaned. The findings demonstrated that sesame attained 98.5% purity at the output rate of 200.6 g/min (12.03 kg/h) while kalonji reached 97.6% seed purity at an output rate of 370.2 g/min (22.2 kg/h). These results demonstrate how important carefully regulated shaker frequency and airflow speed are for improving output quality and cleaning effectiveness. The study shows that the Kimseed MK3 is a suitable low-cost, scalable option for research operations and smallholder farmers, providing better seed quality and processing efficiency for underutilised yet economically valuable oilseed crops. Full article

Agrivoltaic greenhouses in southern Spain offer a sustainable way to manage excessive irradiance levels by generating renewable energy. This study presents a shading experiment on tomato cultivation in a raspa-y-amagado greenhouse in Almeria, southern Spain, during the 2023–2024 growing season. Photovoltaic modules were mimicked by opaque plastic sheets that were arranged in a checkerboard pattern on the roof of the greenhouse. Two shading zones (30% and 50% roof cover ratio) were compared against an unshaded control zone. Microclimate, plant physiology, yield and quality were monitored during the study. The results show that shading influenced the microclimate, which directly impacted crop yield. The 30% and 50% shading zones resulted in 15% and 26% crop yield reductions, respectively. A preliminary, theoretical analysis of potential revenues of the photovoltaic yield showed that reductions in crop yield can be overcompensated by the energy generated by the PV system. For the summer crop cycle, a higher PV production and lower crop yield reductions can be expected. The economic advantage demonstrates the potential of agrivoltaic greenhouses in southern Spain. Full article

This Technical Note presents the design and implementation of a low-cost modular growth chamber developed to keep mother plants under controlled environmental conditions for vegetative propagation. The system was conceived as an accessible alternative to expensive commercial equipment, offering reproducibility and adaptability for small-scale and research-based cultivation. The proposed chamber integrates thermal insulation, LED lighting, forced ventilation through the implementation of extractors, a recirculating irrigation system with double filtration, and a sensor-based environmental monitoring platform operated via an Arduino UNO microcontroller. The design features a removable tray that serves as a support for the mother plant, an observation window covered by a movable dark acrylic that prevents the passage of external light, and a vertical structure that facilitates optimal space utilization and ergonomic access. Functionality was conducted using a Stevia rebaudiana Bertoni mother plant maintained for 30 days under monitored conditions. Environmental parameters—temperature, relative humidity, and illuminance—were recorded continuously. The plant showed vegetative development through new shoot emergence and the growth in height of the plant, and despite a loss in foliage expansion, it confirmed the chamber’s capacity to support sustained growth. Although no statistical replication or control group was included in this preliminary evaluation, the system demonstrates technical feasibility and practical utility. This chamber provides a replicable platform for future experimentation and propagation studies. Complete technical specifications, schematics, and component lists are provided to enable replication and further development by other researchers. The growth chamber design aligns with the goals of open-source agricultural innovation and supports knowledge transfer in controlled-environment plant propagation technologies. Full article

This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: the WRF Control Run, which maintained unchanged LULC, and the WRF Sensitivity Experiment, which converted 56.5% of forested areas into cropland to assess the impact on storm dynamics. Quantitative comparisons of predicted rainfall from both simulations were conducted against observed data. The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. In contrast, the modified scenario yielded an RMSE of 2.01, suggesting lower reliability. The Mean Bias (MB) values were 1.32 for the control run and 1.58 for the modified scenario, revealing notable discrepancies in accuracy. The coefficient of determination (R²) was 0.247 for the control run and 0.270 for the modified scenario. The Nash–Sutcliffe Efficiency (NSE) value was 0.1567 for the control run but dropped to −0.2257 following LULC modifications. Sensitivity analyses revealed a 60% increase in heat flux and a 36% rise in precipitation, underscoring the significant impact of LULC on meteorological outcomes. While this study concentrated on the Black Belt region, the methodologies employed could apply to various other areas, though caution is advised when generalizing these results to different climates and socio-economic contexts. Further research is necessary to enhance the model’s applicability across diverse environments. Full article

This research presents an essential oil (EO) distillation method with improved efficiency, called recurrent water and steam distillation (RWASD), as well as the testing of a 500 L prototype essential oil distillation machine (500 L PDM). The raw material used was 100 kg of lime fruit. At each distillation time point, the test result was compared with that obtained via water and steam distillation (WASD), and different raw material grid configurations were taken into consideration. It was found that distillation using the RWASD method increased the amount of EO obtained from limes by 53.69 ± 2.68% (or 43.21 ± 2.16 mL) compared with WASD. The results of gas chromatography mass spectrometry (GC-MS) analysis of bioactive compounds from the distilled EO revealed that important compounds were present in amounts close to the standards reported in many studies; namely, β-myrcene (2.72%), limonene (20.72%), α-phellandrene (1.27%), and terpinen-4-ol (3.04%). In addition, it was found that the temperature, state of saturated steam, and heat distribution during distillation were relatively constant. The results showed the design, construction, and heat loss error values of the 500 L PDM were 5.90 ± 0.29% and 7.83 ± 0.39%, respectively, leading to the use and percentage of useful heat energy to stabilize at 29,880 ± 1,494 kJ/s and 22.47 ± 1.12%, respectively. Additionally, the shape of the grid containing the raw material affects the temperature distribution and the amount of EO distilled, with values 10.14 ± 0.51% and 8.07 ± 0.40% higher for the normal grid (NS), respectively, as well as an exergy efficiency of 49.97 ± 2.49%. The highest values found for exergy in, exergy out, and exergy loss were 294.29 ± 14.71 kJ/s, 144.76 ± 7.23 kJ/s, and 150.22 ± 7.51 kJ/s, respectively. The obtained results can be further developed and expanded to promote the application of this method in SMEs, serving as basic information for the development of the EO distillation industry. Full article

This study introduces a comprehensive framework for assessing and enhancing the quality of hourly temperature data collected from a six-station agrometeorological network in the Arta plain, Epirus, Greece, spanning the period 2015–2023. By combining traditional quality control (QC) techniques with advanced methods—Dynamic Time Warping (DTW), Fuzzy Logic, and XGBoost machine learning—the framework effectively identifies anomalies and reconstructs missing or erroneous temperature values. The DTW–Fuzzy Logic approach reliably detected spatial inconsistencies, while the machine learning reconstruction achieved low root mean squared error (RMSE) values (0.40–0.66 °C), ensuring the high fidelity of the corrected dataset. A Data Quality Index (DQI) was developed to quantify improvements in both completeness and accuracy, providing a transparent and standardized metric for end users. The enhanced temperature data significantly improve the reliability of inputs for applications such as evapotranspiration (ET) estimation and agricultural decision support systems (DSS). Designed to be scalable and automated, the proposed framework ensures robust Internal Consistency across the network—even when stations are intermittently offline—yielding direct benefits for irrigation water management, as well as broader agrometeorological applications. Full article

Downy mildew and powdery mildew are among the most serious diseases that affect grapevine. They can cause severe damage, such as yield loss, and affect the size of the grapes and their ability to accumulate sugars, affecting the flavor and aroma negatively and increasing the need for fungicidal sprays to combat these diseases and the pathogens that cause them. Clearly, it is important to predict these diseases early and apply treatment promptly to prevent and mitigate the effects of these diseases on crop production. This study presents a workflow in which IoT environmental sensors and machine learning methods are leveraged to accurately predict disease onset and allow for timely fungicide applications or other disease management strategies. We collected IoT grapevine field measurements and leveraged the records of the respective time periods during which fungicide treatments were applied to grapevine, and we used them to train and evaluate different ML tabular data classifiers as early predictors for each of the two diseases. The TabPFN transformer demonstrated superior performance in disease risk assessment while enabling real-time predictions with sub-second latencies, so it can be considered as a very good choice for a real-time grapevine disease prediction system. Full article

Reusing agro-industrial by-products is a successful strategy that aligns with the 2030 Sustainable Development Goals. Red grape pomace poses a significant environmental challenge, particularly for wine-producing nations. Due to its high moisture content and seasonal availability, ensiling emerges as a potential method to prolong the nutritional value of red grape pomace, supporting the need for research into its application in animal nutrition. This study analyzed the bale ensiling process for red grape pomace by assessing its potential integration into ruminant diets and comparing its storage stability to untreated preservation methods. Baled silage units (approximately 300 kg each) were employed for this purpose. Analytical evaluations were conducted at 0, 7, 14, 35, 60, and 180 days of storage to monitor microbial and fermentation activity, nutritional composition, and bioactive attributes. Bale silage preserved the nutritional and microbial quality of red grape pomace for ruminant feed over a storage period of 180 days. The results demonstrated that bale silage successfully maintained the macro-composition, bioactive compounds, and antioxidant properties while reducing the fatty acid profile’s susceptibility to oxidation. By contrast, untreated storage led to significant spoilage. We concluded that bale ensiling is a suitable and effective technique that preserves red grapes for ruminant feed over a long period. Full article

Forage scarcity, intensified by climate variability and edaphoclimatic limitations in the Brazilian semiarid region, challenges regional livestock production. In this context, forage palm is a strategic alternative due to its drought resistance and environmental adaptability. However, little is known about the spatial and temporal dynamics of its cultivation. This study aimed to characterize the spatio-temporal dynamics of forage palm cultivation in Capoeiras-PE between 2019 and 2022 using remote sensing data and multitemporal analysis of the Normalized Difference Vegetation Index (NDVI), processed via Google Earth Engine. Experimental areas with Opuntia stricta (“Mexican Elephant Ear”) and Nopalea cochenillifera (“Miúda”) were monitored, with field validation and descriptive statistical analysis. NDVI values ranged from −0.27 to 0.93, influenced by rainfall, cultivar morphology, and seasonal conditions. The “Miúda” cultivar showed a lower coefficient of variation (CV%), indicating greater spectral stability, while “Orelha de Elefante Mexicana” was more sensitive to climate and management, showing a higher CV%. Land use and land cover (LULC) analysis indicated increased sparse vegetation and exposed soil, suggesting intensified anthropogenic activity in the Caatinga biome. Reclassified NDVI enabled spatial estimation of forage palm, despite sensor resolution and spectral similarity with other vegetation. The integrated use of satellite data, field validation, and geoprocessing tools proved effective for agricultural monitoring and territorial planning. Full article

Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. Three inputs were used for each model: leaf reflectance, peel reflectance, and fruit reflectance. The machine learning model SVM, stood out for its best results, demonstrating good generalization capacity and effectiveness in predicting these attributes, reaching accuracy values above 0.7 for Brix and ratio, using fruit reflectance. In terms of the overall efficiency of the input variables, peel and fruit were the most informative, with peel standing out for the estimation of secondary metabolism compounds, while the fruit showed excellent performance in predicting flavor-related attributes, such as acidity, °Brix and ratio, as mentioned previously, above 0.7. These results highlight the potential of using spectral data and machine learning in the non-destructive assessment of pineapple quality, enabling advances in monitoring and selecting fruits with better sensors. Full article

This study resulted from the demand for seeds with physiological qualities and studies in germination tests applied for seed improvement aimed at productive and homogeneous harvests. The objective of this study was to improve the classification of seeds in germination tests by introducing YOLO as a classification tool for germinated or nongerminated seeds to specify the results and optimize the analysis period. Germination tests were performed for Glycine max (soybean) seeds, and the capture of images from the tests and conventional categorization was performed by uncorrelated individuals, for the processing of these images and application to YOLO. Subsequently, graphical analyses of the YOLO results and comparison metrics with conventional categorization were performed to determine the accuracy of YOLO as a seed categorization tool. The results derived from the analysis of the graphs and comparisons to the conventional methodology of seed classification showed the effectiveness of YOLO for classifying seeds as germinated or nongerminated, reaching 95% accuracy in seed classification, beyond the range of 0–0.110 of the prediction errors, determined by the application of the methodology of mean square error, highlighting the efficiency of YOLO. Full article

Anti-insect nets are characterized by a very low porosity that determines a variation in the microclimate below the protection in terms of an increase in the relative humidity, a reduction in air ventilation, and a temperature rise. The air permeability of the textile depends on numerous factors such as the thickness of the wires, the size of the holes, the porosity, and the air velocity. The knowledge of this relationship would make it possible to optimize the size of the holes in order to maintain the anti-insect function with the increase in air velocity. The air permeability coefficients of 10 anti-insect nets were evaluated by means of a micro wind tunnel. The results showed that the loss coefficient is linked to the porosity (ε) of the nets: as the porosity increases, the loss coefficient decreases. The parameter that demonstrated the strongest correlation with the loss coefficient was the function of porosity h(ε) = (1 − ε²)/ε². In the interval of porosity $0.10<\epsilon <0.60$, the linear regression correlation is quite high ($R^2=0.87$). Finally, the reduction factor RF(ε)—an estimation of the reduction in wind pressure acting perpendicularly on the surface of a textile due to its porosity—was calculated and compared with that proposed by the Australian standard, which, currently, is the only international standard that explicitly considers the effect of porosity on wind action. Full article

This article presents a systematic literature review on Information and Communication Technologies (ICTs) applied to precision agriculture, focusing on their relevance to Colombia. It identifies key technical and administrative needs for digital transformation in the sector and proposes a conceptual roadmap for implementation. Findings highlight the potential of early warning systems (EWSs), the Internet of Things (IoT), and artificial intelligence (AI) to improve productivity, sustainability, and climate resilience. The study outlines current adoption barriers and proposes future empirical validation through field experiments. Full article

Tomatoes (Solanum lycopersicum) and potatoes (Solanum tuberosum) are vital staple crops. They are prone to diseases from pathogens like Ralstonia and Fusarium, which cause significant agricultural losses. Detecting volatile organic compounds (VOCs) emitted by plants under stress offers a promising approach for advanced monitoring of crop health. This study examines sensing materials for wearable plant sensors targeting VOCs as biomarkers under abiotic and biotic stress. Key questions addressed include the specific VOC emission profiles of potato and tomato cultivars, how materials and sensing mechanisms influence sensor performance, and material considerations for agricultural use. The analysis reveals cultivar-specific VOC profiles under stress, challenging the identification of universal biomarkers for specific diseases. Through a literature review, this study reviews VOC responses to fungi, bacteria, and viruses, and compares non-composite and hybrid chemiresistive and electrochemical sensors based on sensitivity, selectivity, detection limits, response time, robustness, cost-effectiveness, and biocompatibility. A superstructure bridging materials science, plant pathology, AI, data science, and manufacturing is proposed, emphasizing three strategies: sensitivity, flexibility, and sustainability. This study identifies recent research trends that involve developing biodegradable wearable sensors for precision agriculture, leveraging flexible biocompatible materials, multi-parameter monitoring, self-healing properties, 3D-printed designs, advanced nanomaterials, and energy-harvesting technologies. Full article

This study examined the effectiveness of using Internet of Things (IoT) technology to control environmental conditions in open-air pig housing systems in Thailand. This experiment was conducted in three zones: Zone 1, with no environmental controls (natural ventilation); Zone 2, with ventilation fans but no water-spraying system; and Zone 3, equipped with both ventilation fans and a roof-mounted water-spraying system. Key parameters, such as ammonia (NH₃), hydrogen sulfide (H₂S), temperature, and relative humidity, were monitored all year round. Zone 1, with only natural ventilation, exhibited the highest levels of pollutants, with an average ammonia concentration of 7.1 ppm and hydrogen sulfide at 7.6 ppm. The temperature averaged 31.81 °C, and the relative humidity was 53.65%, creating unfavorable conditions for pig farming. Zone 2, featuring ventilation fans, showed improvements, with the average ammonia and hydrogen sulfide levels reduced to 3.75 ppm and 4.12 ppm, respectively, although the temperatures (29.35 °C) were still too high at times, and the relative humidity was 49.50%. Zone 3, incorporating both fans and a water-spraying system, demonstrated the most effective environmental control, achieving lower ammonia (3.0 ppm) and hydrogen sulfide (2.93 ppm) levels, with an average temperature of 28.85 °C and relative humidity of 47.15%. These results suggest that IoT technology, combined with adequate ventilation and cooling systems, significantly enhances environmental conditions, thereby promoting better pig health and growth. Full article

Porosity is the key factor affecting a medium’s tortuosity, effective evaporation area coefficient, and ventilation resistance, and further affects the drying efficiency, energy consumption, and drying uniformity in the drying process. To reveal the dynamic variation characteristics of porosity in paddy flow layer, an air convection drying apparatus was established and a mathematical porosity model was established based on response surface methodology. The reliability of the model was verified through EDEM–Fluent coupled digital simulation and experiments. The research results show that under different paddy flow rates v_d (0.01 m/s, 0.03 m/s, 0.05 m/s), different moisture contents M_c (14% w.b., 23% w.b., 32% w.b.), different wind speeds v_w (0.4 m/s, 0.6 m/s, 0.8 m/s), and different layer thicknesses L (100 mm, 150 mm, 200 mm), the porosity values obtained by the porosity measurement device range from 39.562% to 46.006%. The relative errors between the actual values (ε_r), the simulation values (ε_s), and the predicted values (ε_p) are all within ±1%. Moreover, the obtained mathematical porosity model has high reliability (R² = 0.968). The Conclusions provide an analysis method for dynamic change characteristic parameters and basic data for the dynamic change of porosity to reduce drying energy consumption, improve the drying power coefficient, and enhance drying quality. Full article