Research in Agricultural Engineering - In Press
The Auto Sprinkler Rover: An Innovative Fertilizer Applicator for Sustainable Agriculture Original Paper
Wan khaima azira Wan mat khalir, ISMAIL AZIMAH, Nur Maizatul Akma Abdul Aziz (email: nurmaizatulakma011@ gmail.com)
Fertilizers are essential to agriculture since they provide crucial nutrients that stimulate crop growth, increase yield and improve yield quality. However, this practice requires the use of manual labour, which consumes energy and time as well as limits the effectiveness of fertilization methods and is environmentally harmful. These factors have led to the development of an innovative product based on the Internet of Things (IoT) called the Auto Sprinkler Rover, which is a remote controlled machine that operates automatically. It contains a 12 L water barrel that serves as a storage tank for liquid fertilizer. This study had designed a liquid sensor and light emitting diode to alert the user to the amount of liquid in the tank. The water pump facilitates the smooth spraying of liquid fertilizer from the pipe’s nozzle. Auto Sprinkler Rover differs from conventional methods because it integrates real-time monitoring via Wi-Fi, an automatic liquid level sensor as well as an automatic spraying system and ergonomic design that is manually operated. The Auto Sprinkler Rover was designed to be a cutting-edge technology that benefits sustainable crop cultivation for households, communities and the global agriculture industry.
A Spectral Signature-Based Algorithm for Identifiability of Crops and Their Cultivation ConditionsOriginal Paper
Sarah El Azizi, Halima Taia, Abdes-Samed Bernoussi, Mina Amharref, Edyta Wozniak
Recent advancements in remote sensing techniques, especially the combination of hyperspectral imaging with analytical algorithms, have greatly improved precision agriculture. This study introduces some algorithms developed for identifying crops and evaluating their growth conditions, focusing on irrigation and fertilisation. The present approach is based on the concept of identifiability of a family of dynamic systems and the differentiation of plants using their spectral signatures. The method uses a repository of spectral data and applies a developed algorithm to compare the measured spectra with the reference database, enabling the identifiability and the recognition of both known and unknown crops. As an application of our approach, we have considered two different crops: mint and rosemary, under different irrigation and fertilisation conditions. The results show that the algorithm achieved a 100% identification rate across the four unknown samples. The minimum spectral distances obtained are 0.01 and 0.03 for rosemary and mint respectively. Thus, the family of systems was identifiable with a tolerance of η < 0.03. The study concluded that the algorithm effectively classifies the crop type and deduces its growth conditions, demonstrating its effectiveness for agricultural monitoring.
Prediction of Postharvest Fruit Quality Using Machine Learning and Spectral Imaging DataOriginal Paper
Mardiantono Mardiantono, Zulfahrizal Zulfahrizal
Postharvest quality assessment of tropical fruits is crucial for preserving market value and ensuring consumer acceptance; however, conventional evaluation methods are destructive and unsuitable for real-time applications. This study developed and compared four machine learning (ML) algorithms—Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—for non-destructive prediction of fruit quality using spectral imaging data. Publicly available datasets of mango, papaya, and banana covering the 400–1000 nm wavelength range were employed. Both continuous attributes (soluble solids content, firmness, and moisture content) and categorical targets (ripeness and firmness classes) were evaluated. Spectral data were pre-processed using noise reduction, standardization, and dimensionality reduction techniques, and model performance was assessed using five-fold cross-validation. The results showed that ANN and XGBoost consistently outperformed SVM and RF for both regression and classification tasks. Feature importance analysis revealed that wavelengths in the 700–900 nm range were particularly informative, reflecting their association with sugar and water absorption characteristics in fruit tissues. These findings demonstrate the strong potential of integrating spectral imaging and machine learning for accurate, non-destructive fruit quality assessment. Nevertheless, further validation under field conditions and expansion to a wider range of fruit types are recommended to enhance model robustness and practical applicability.
Estimating changes in the Khisar glacier, using remote sensing data and GIS technologies for assesment of water use in agriculture (Surkhandarya valley, Uzbekistan)Original Paper
Shokhjakhon Khamidullaev, Rustam Oymatov, Ilhom Abdurahmanov, Ilkhom Aslanov
Climate change is speeding up the melting and retreat of glaciers, which is a big threat to water security in dry and semi-dry areas like Uzbekistan. To understand how glaciers affect regional hydrological systems and to come up with adaptive water management strategies, it is important to keep an eye on how they change over time. This study examines the temporal changes of the Khisar Glacier in the Surkhandarya Basin by combining remote sensing data from different times with GIS-based spatial analysis. We looked at Landsat images from 1990, 2000, 2010, and 2024 to see how the size of glaciers has changed and how that relates to weather and water variables. The results show that the glacier area has shrunk significantly, from 8.6 km² in 1990 to 5.1 km² in 2024, a 40.7% decrease over the past three decades. The mean annual temperature in the basin rose by about 1.9 °C during the same time, and the Surkhandarya River's average summer discharge fell by about 22%. These results show how closely rising temperatures, melting glaciers, and lower river flow are linked. They also show how vulnerable glacier-fed water systems are to climate change. Combining satellite observations with climate and hydrology data is a good way to keep an eye on glaciers and assess water resources over time. The GIS-based monitoring framework created in this study provides useful tools for planning how to adapt to climate change and manage water resources in a way that is good for the environment in the Surkhandarya region and other glacier-dependent basins in Central Asia.
Research on the Optimal Design and Process Parameters of a Castor Seed Cleaning MachineOriginal Paper
Elchyn Aliiev, Valentyn Holovchenko, Olha Aliieva
In the context of the modern EU bioeconomy, the use of industrial crops, particularly castor bean (Ricinus communis L.), is relevant for the production of industrial and energy products without competing with food crops. Castor oil is used for the production of biodiesel, lubricants, paints and coatings, cosmetics, and pharmaceuticals. In Ukraine, castor can be cultivated on low-yield soils, with seed yields ranging from 1.5 to 2.1 t/ha depending on the variety. However, the morphological features of the fruits and uneven ripening complicate the mechanization of harvesting and seed cleaning processes. Existing equipment designed for other crops cannot be applied due to the risk of damaging castor seeds. A design of a castor seed cleaning machine has been proposed, combining mechanical fruit shelling with aerodynamic separation of the mixture. The machine is equipped with an eccentric crushing cone, rubber linings, a pneumatic separation channel, a cyclone, and an automated control unit. As a result of numerical modeling and experimental studies of the process of separating and cleaning castor seeds, dependencies were obtained for the productivity of the developed machine Q, power consumption P, specific energy consumption E, fraction of unshelled fruits (segments) ξf, content of clean seed in the seed collector ψs-s depending on the distance between the reverse and crushing cones δ, rotation frequency of the crushing cone n, diameter of the feed opening Din, inclination angle of the crushing cone axis γ, and airflow velocity V. A multi-criteria optimization method was applied to find the optimal operating modes: δ = 10.8 mm; n = 282 rpm; V = 3.6 m/s; Din = 98 mm; γ = 3.6°; β = 20.3°. The following results were achieved: E = 0.0394 MJ/kg; Q = 163.4 kg/h; P = 1861 W; ξf = 0.099; ψs-s = 0.958. The obtained results confirm the efficiency of the proposed design for industrial implementation.
The effects of temperature on biogas production rate and purityOriginal Paper
Roger Jay Lamadrid De Vela, Romeica Noe Rimorin, Christian Mark Felix
This study investigated the effects of temperature on the performance of anaerobic digesters for biogas production. Digesters were filled with a 1:1 ratio of substrate to water, containing 15 kg of cow dung and 3 kg of crop waste, and maintained at temperatures of 50±2°C and 30±2°C, corresponding to the thermophilic and mesophilic biodigesters, respectively. The experiments run for 75 days, and biogas production rate and purity were measured. The thermophilic digester produced 48.4% more biogas and had a slightly higher pH (7.65) than did the mesophilic digester (7.37) by the end of the observation period. However, gas chromatography revealed that the CH4 and CO2 contents did not significantly differ between the two treatments. The CH4 concentration in the mesophilic environment was 42±10%, whereas that in the thermophilic environment was 53.5±10%. The CO2 composition was 32.5±1% and 35.5±1% for the mesophilic and thermophilic setups, respectively. These were supported by the wavelength (460 to 620 nm) of the flame color, indicating that the biogas from both set-ups is predominantly composed of methane. In conclusion, thermophilic anaerobic digesters may have a relatively high biogas production rate, but the biogas purity is not significantly different from that of mesophilic digesters.
Modelling the Hydration Process of Wheat Grain with Layer- Dependent Diffusion CoefficientsOriginal Paper
Urinboev Abdushukur, Bakhtiyar Ismailov, Khairulla Ismailov, Akmaljon Kuchkarov
This study presents the development and validation of a mathematical model for simulating the hydration process of wheat grain, accounting for its anatomically heterogeneous, multilayered structure with layer- dependent radial diffusion coefficients, experimentally determined for bran, endosperm, and germ. Radial moisture transport is described using Fick’s law, and numerical solutions were obtained by combining Fourier series methods with finite difference approximations. Two approaches to interlayer moisture exchange are considered. In the basic variant, continuity of concentration and flux at layer boundaries is assumed, corresponding to the classical radial diffusion model with piecewise-constant coefficients. In the enhanced variant, the presence of a moisture-retaining film is taken into account, introducing additional diffusion resistance at interlayer boundaries. Modified coupling conditions are thereby applied, allowing concentration discontinuities and describing resistance to moisture transfer through an interfacial mass transfer coefficient and film resistance. A 3D model of wheat grain was constructed using COMSOL Multiphysics together with 3D scanning techniques (structured-light imaging and microscopy). The resulting data enabled a quantitative description of moisture distribution within each layer and verification of the model against microscopic analysis. The developed model has been integrated into the framework of an automated control system for pre-milling grain hydration, ensuring accurate and responsive moisture regulation. This approach substantially improves efficiency and stability in flour milling operations and can be adapted to specific wheat varieties and storage conditions, as well as extended for three-dimensional and transient modelling tasks.