As we already know that agriculture engineering is very close with Environment and food product that highly connected with bio diversity and human ...

I know that IOT is not just limited to Computer Science & Engineering its been applied at Agriculture Engineering as Well. And Smart I...

A recent study in AgriEngineering by Muhammad Irfan and Gamal El Afandi found that buried biochar interlayers effectively block capillary rise, reducing topsoil salinity by up to 31.6 percent. This…

A recent study in AgriEngineering by Suarez et al. (2026) shows that biochar application rates up to 44.8 megagrams per hectare did not significantly alter soil health indicators in a sandy loam Ul…

A recent study in AgriEngineering by Suarez et al. (2026) shows that biochar application rates up to 44.8 megagrams per hectare did not significantly alter soil health indicators in a sandy loam Ul…

This review aims to study the applications of sensors for monitoring and controlling the physicochemical parameters of water in aquaculture systems such as Biofloc Technology (BFT), Recirculating Aquaculture Systems (RASs), and aquaponic systems using IoT technology, as well as identify potential knowledge gaps. A bibliometric analysis and systematic review were conducted using the Scopus database between 2020 and 2024. A total of 217 articles were reviewed and analyzed. Our findings indicated a significant increase (74.79%) in research between 2020 and 2024. pH was the most studied physicochemical parameter in aquaculture, analyzed in 98.2% of cases (sensors: SEN0169, HI-98107, pH-4502C, Grove-pH), followed by temperature (92.9%, sensor DS18B20) and dissolved oxygen (62.5%, sensors: SEN0237, MAX30102, OxyGuard DO model 420, ZTWL-SZO2-485, Lutron DO-5509). Overall, water monitoring through the implementation of IoT sensors improved growth rates, reduced culture mortality rates, and enabled the rapid prediction and detection of atypical Total Ammonia Nitrogen (TAN) levels. IoT sensors for water quality monitoring in aquaponics also facilitate the evaluation and prediction of seed and vegetable growth and germination. In conclusion, despite recent advancements, challenges remain in automating parameter control, ensuring effective sensor maintenance, and improving operability in rural areas, which need to be addressed.

Achieving global sustainable agriculture requires farmers worldwide to adopt smart agricultural technologies, such as autonomous ground robots. However, most ground robots are either task- or crop-specific and expensive for small-scale farmers and smallholders. Therefore, there is a need for cost-effective robotic platforms that are modular by design and can be easily adapted to varying tasks and crops. This paper describes the hardware design of a unique, low-cost multiaxial modular agricultural robot (ModagRobot), and its field evaluation for soybean phenotyping. The ModagRobot’s chassis was designed without any welded components, making it easy to adjust trackwidth, height, ground clearance, and length. For this experiment, the ModagRobot was equipped with an RGB-Depth (RGB-D) sensor and adapted to safely navigate over soybean rows to collect RGB-D images for estimating soybean phenotypic traits. RGB images were processed using the Excess Green Index to estimate the percent canopy ground coverage area. 3D point clouds generated from RGB-D images were used to estimate canopy height (CH) and the 3D Profile Index of sample plots using linear regression. Aboveground biomass (AGB) was estimated using extracted phenotypic traits. Results showed an R2, RMSE, and RRMSE of 0.786, 0.0181 m, and 2.47%, respectively, between estimated CH and measured CH. AGB estimated using all extracted traits showed an R2, RMSE, and RRMSE of 0.59, 0.0742 kg/m2, and 8.05%, respectively, compared to the measured AGB. The results demonstrate the effectiveness of the ModagRobot for in-row crop phenotyping.

Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil’s physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R2 values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (−0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices.

Effective water and soil management is crucial for crop productivity, particularly in rice cultivation, where poor soil quality and water scarcity pose challenges. The response of deeper-rooted rice grown in soils amended with different soil amendments (SAs) to Internet of Things (IoT)-managed alternate wetting and drying (AWD) irrigations remains undetermined. This study explores the effects of various SAs on DRO-1 IR64 rice plants under IoT-based soil moisture monitoring of AWD irrigation. A greenhouse experiment executed at the Tokyo University of Agriculture assessed two water management regimes—continuous flooding (CF) and AWD—alongside six types of SAs: vermicompost and peat moss (S + VC + PM), spirulina powder (S + SPP), gypsum (S + GS), rice husk biochar (S + RHB), zeolite (S + ZL), and soil without amendment (S + WA). Soil water content was continuously monitored at 10 cm depth using TEROS 10 probes, with data logged via a ZL6 device and managed through the ZENTRA Cloud application (METER GROUP Company). Under AWD conditions, VC + PM showed the greatest decline in volumetric water content due to enhanced root development and water uptake. In contrast, SPP and ZL maintained consistent water levels. Organic amendments like VC + PM improved soil properties and grain yield, while AWD with ZL and GS optimized water use. Strong associations exist between root traits, biomass, and grain yield. These findings highlight the benefits of integrating SAs for improved productivity in drought-prone rice systems.

Stable pear cultivation relies on cross-pollination, which typically depends on insects or wind. However, natural pollination is often inconsistent due to environmental factors such as temperature and humidity. To ensure reliable fruit set, artificial pollination methods such as wind-powered pollen sprayers are widely used. While effective, these methods require significant labor and operational costs, highlighting the need for a more efficient alternative. To address this issue, this study aims to develop a fully automated drone-based pollination system that integrates Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs). The system is designed to perform artificial pollination while maintaining conventional pear cultivation practices. Demonstration experiments were conducted to evaluate the system’s effectiveness. Results showed that drone pollination achieved a fruit set rate comparable to conventional methods, confirming its feasibility as a labor-saving alternative. This study establishes a practical drone pollination system that eliminates the need for wind, insects, or human labor. By maintaining traditional cultivation practices while improving efficiency, this technology offers a promising solution for sustainable pear production.

In agricultural planting, early plant classification is an indicator of crop health and growth. In order to accurately classify early plants, this paper proposes a classification method combining a dual attention mechanism and multi-scale module. Firstly, the ECA module (Efficient channel attention) is added to enhance the attention of the network to plants and suppress irrelevant background noise; secondly, the MSFN (Multi-scale Feedforward Network) module is embedded to improve the ability to capture complex data features. Finally, CA (Channel attention) is added to further emphasize the extracted features, thus enhancing the discrimination ability and improving the accuracy of the model. The experimental results show an accuracy of 93.20%, precision of 94.53%, recall of 93.27%, and an F1 score of 93.39%. This study can realize the classification of early plants, and effectively distinguish crops from weeds, which is helpful to identify and realize accurate weeding, thus promoting the intelligent and modern process of agricultural production.

Thyme (Thymus vulgaris) was dried using a tray dryer, recirculating tray dryer, and vacuum dryer at 35 °C, 40 °C, and 45 °C, respectively. The dried thyme after attaining 5% moisture content was subjected to a grinding process to obtain powder using a hammer mill for further analysis of physiochemical properties, bioactive compounds, and techno-functional properties. The ash content was 10.21%, fiber content was 13.57%, fat content was 1.69%, protein content was 5.61%, and carbohydrate content was 22.91% for the thyme sample dried at 35 °C via vacuum drying. Meanwhile, regarding the functional properties, the swelling power was 0.31%, dispersibility was 27.72%, emulsion capacity was 35.44%, foam capacity was 35.47%, and foam stability was 1.84% for the thyme sample dried at 40 °C in the vacuum dryer. The total chlorophyll content, ascorbic acid content, and bioactive compounds were retained best in the vacuum-dried sample at 40 °C. Bioactive compound retention for VDT among the selected three techniques at 35 °C was considerably better. The color values were found to be similar to those of freshly harvested thyme (hue, 93.39; chroma, 3.47) for the thyme sample dried at 40 °C in a vacuum dryer. Based on the analysis, it was found that vacuum drying at 40 °C gave better results, followed by the recirculating tray dryer at 40 °C and the tray dryer at 40 °C. The adequately dried thyme samples with the time–temperature combinations for different drying techniques used can be used further for product development and studies on their shelf life.

Early diagnosis of subclinical ketosis is fundamental in the production management of dairy cattle. Without evident clinical signs, this pathological condition causes important economic losses for the farmer and significant health repercussions for the cattle that could develop an altered immune function. Laboratory techniques, although accurate, are expensive, invasive, and cannot be used for real-time monitoring of the entire herd. On the contrary, the analysis of volatile organic compounds (VOCs) contained in the breath of dairy cattle affected by ketosis could represent a key biomarker of the ketogenic process. For this reason, we developed a sensory device, tested in the laboratory, to detect acetone concentrations ranging from 1 to 10 ppm (concentrations typically detected in the cow’s breath), and we look to verify the electronic nose’s potential as a non-invasive diagnostic tool for ketosis. Experimental results show the high sensitivity of the instrument in differentiating acetone solutions. Principal component analysis (PCA) showed a clear separation of samples in the score plot, while classification using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) achieved accuracy rates above 70% and 85%, respectively. These findings suggest the potential application of the electronic nose as a non-invasive diagnostic tool in veterinary diagnostic studies. In particular, its ability to detect and discriminate low acetone concentrations could help the farmer to improve the overall management of the herd, optimising monitoring strategies and ketosis diagnosis before the appearance of the clinical signs of the disease.

This paper presents research on the impact of vibrations on the hand–arm and body system of agricultural tractor operators as ergonomic indicators in relation to certain operational parameters. The measurements were conducted on a LANDINI POWERFARM 100 tractor on agricultural production areas and access roads of the Agricultural and Veterinary School in Osijek. The measurements followed the ISO 5008:2015 standard, which describes the creation of test tracks: a smooth track of 100 m in length and a rough track of 35 m in length. Body vibration measurements were conducted according to the prescribed standards HRN ISO 2631-1: 1999/A1:2019 and HRN ISO 2631-4:2010. Hand–arm system vibration measurements were performed according to the prescribed standards HRN ISO 5349-1:2008 and HRN ISO 5349-2:2008/A1:2015. After the measured data were processed, a three-factor analysis of variance was performed, where some operational parameters were designated as A—agrotechnical surfaces (6 types), B—tractor speed (6 speeds), and C—tire air pressure (3 pressures), along with multiple regression analysis and the AHP (analytical hierarchical process). This research determined that none of the measured hand–arm system vibrations exceeded the warning (2.5 ms−²) or limit (5 ms−²) values of daily exposure. Furthermore, vibrations affecting the operator’s body in the x-axis at higher speeds and pressures C2 and C3, in the y-axis at higher speeds and pressures C1 and C2, and in the z-axis at the highest speed and pressures C1 and C2 were found to exceed the daily exposure warning value of 0.5 ms−². It was concluded that the operator’s health is at risk, and it is recommended that the seat’s air suspension system be inspected to prevent further complications in a timely manner.

A recent study in AgriEngineering by Muhammad Irfan and Gamal El Afandi found that buried biochar interlayers effectively block capillary rise, reducing topsoil salinity by up to 31.6 percent. This…

A recent study in AgriEngineering by Rubel et al. found that industrial pelletization significantly reduces the microbial viability of biochar-based biofertilizers. Specifically, the process caused…

Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding.

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A recent study in AgriEngineering by Rubel et al. found that industrial pelletization significantly reduces the microbial viability of biochar-based biofertilizers. Specifically, the process caused…