Research in Agricultural Engineering - In Press
Optimization of Irrigation requirement of Okra under Protected Cultivation using Digital LysimeterOriginal Paper
Sujitha Elango, Nagarajan Madasamy, Valliammai Annamalai, Vijayaprabhakar Arumugam
A field experiment was conducted in 2023 and 2024 to determine stage-specific crop coefficient values of okra (Abelmoschus esculentus) using the popular F1 hybrid Arka Nikita. Six evapotranspiration (ETc) based treatments were applied: five under a forced ventilated Greenhouse (T1 = 120% ETc, T2 = 100% ETc, T3 = 80% ETc, T4 = 60% ETc, T5 = 100% ETc in lysimeter) and one under open field (T6 = 100% ETc) arranged in completely randomized block design with three replications. Results showed that T2 had higher growth parameters, while T4 and T6 performed poorly. Yield was significantly higher in T2 (23.8 t/ ha in 2023 and 23.3 t/ ha in 2024), whereas T6 had a lower (9.5 t/ ha in 2023 and 8.6 t/ ha in 2024). The higher water productivity was observed in T3 (9.85 kg/ m3 in 2023 and 8.35 kg/ m3 in 2024), while T6 had the lower (1.83 kg/ m3 in 2023 and 1.35 kg/ m3 in 2024). Hence, this study recommends using stage-specific crop coefficients of 0.32, 0.63, 0.78, and 0.41 during the initial development, mid and final stages of 80% ETc to optimize water productivity and maximize yield in the greenhouse-grown okra.
Biodegradable Seedling Pots from Sawdust and Spent Mushroom CompostOriginal Paper
Francis Kumi, Joseph Conduah, Hans Murangaza, Seth Osei
Circular bioeconomy is rapidly gaining grounds in the agricultural sector with priority given to the utilization of more environmentally friendly materials for production and processing. Thus in this study, biodegradable seedling pots were developed using sawdust (SD) and spent mushroom compost (SMC) as a sustainable alternative to plastic containers. Four pots composed of SMC:SD ratios of 100:0, 70:30, 60:40, and 50:50 were developed and evaluated. Their mechanical properties, structural characteristics, water absorption capacity pots were assessed and seedlings made to grow in them to monitor growth support potential. A universal tensile test machine was used to assess the indirect tensile strength (mechanical properties) while the samples scanning electron microscope was used to examine the morphology of the samples. Also, images of seedling roots were segmented and analyzed in ImageJ and WinRHIZO softwares to determine the root system architecture. The results demonstrated that the 60:40 ratio exhibited superior performance including optimal water absorption capacity, indirect tensile strength, and structural properties. The 70:30 ratio also showed comparable tensile strength values. However, increasing SMC content in the pot improved root developments. This research presents a viable solution for converting agricultural waste into environmentally friendly seedling containers and suggest a potential option for reducing dependency on plastic pots in agriculture.
Effect of foam-mat drying conditions on drying rate and anthocyanin content in purple sweet potato powderOriginal Paper
Chi Dung Nguyen, Hong Van Hao, Ngoc Giau Tran, Minh Thuy Nguyen, Ngo Van Tai
The study aimed to optimize foam-mat drying parameters for producing purple-fleshed sweet potato (PFSP) powder. Egg albumin (EA) (5-15%), xanthan gum (XG) (0.1-0.5%), and drying temperature (50-70 °C) were used as independent variables for optimization via Response Surface Methodology with a Box-Behnken design. The response variables (drying rate and anthocyanin content) were assessed by 18 treatments, which included 6 central points. The analysis of variance showed a high coefficient of determination (> 88%) between predicted and experimental values across all models. Optimal foam-mat drying conditions were 11.02% EA, 0.34% XG, and 65.1 °C to achieve the highest drying rate (2.49 g water.g dry matter-1.min-1) and anthocyanin content (1.01 mg.g-1). After 3.5 hours of drying at 65.1 °C, the foam-mat dried PFSP showed a low moisture content (4.35%) and water activity (0.29). Its water solubility index, water absorption index, rehydration ratio, total polyphenols, and antioxidant activity were determined to be 56.49%, 3.55%, 3.82, 3.66 ± 0.06 mg GAE.g-1, and 58.49 ± 0.88%, respectively. Under these conditions, the powder maintained its natural beautiful and characteristic purple color. The microstructure of the foam-mat dried PFSP powder (via SEM images) was also observed.
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.
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.
A Novel Ensemble Convolutional Neural Networks for Rice Disease IdentificationOriginal Paper
Richard Alvin Pratama, Nabila Husna Shabrina
Rice is a crucial food commodity worldwide, particularly in Asian countries. However, various factors such as drought, floods, and pest attacks can lead to the emergence of diseases in rice plants. Accurately identifying these diseases poses a significant challenge for farmers, often leading to significant yield losses. Conventionally, farmers rely on manual methods based on their experience and visual inspection to identify rice diseases. However, this approach is highly ineffective, time-consuming, and prone to error. This study aimed to address this issue by proposing advanced deep learning techniques, an ensemble learning method, to automate and enhance the identification of rice plant diseases. The ensemble learning method was proposed by leveraging two state-of-the-art pre-trained models: EfficientNetV2B0 and MobileNetV3-Large. The proposed Average Ensemble method demonstrates superior performance compared with single models. The proposed Average Ensemble achieved superior performance with an average precision of 0.9339, recall of 0.9330, F1-score of 0.9328, and test accuracy of 0.9330. The results of this study can be used to aid farmers and researchers in accurately identifying rice diseases, ultimately supporting better disease management practices, and enhancing agricultural productivity.