Fertilisers are essential to agriculture since they provide crucial nutrients that stimulate crop growth, increase the yield and improve the yield quality. However, this practice requires the use of manual labour, which consumes energy and time as well as limits the effectiveness of fertilisation 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 a liquid fertiliser. This study designed a liquid sensor and light emitting diode to alert th-e user to the amount of liquid in the tank. The water pump facilitates the smooth spraying of liquid fertiliser from the pipe's nozzle. The 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 an 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.
Mechanical sugarcane harvesting generates substantial material losses that are associated with the equipment age. This study evaluated the relationship between the harvester service life and the operational efficiency by analysing field losses and product contamination across machines with varying operational histories (1, 14, 16, and 17 years) in Chaiyaphum Province, Thailand, using a randomised complete block design. The results indicate that the 17-year-old machines exhibited 54% higher total losses (241.93 kg.ha-1) compared to the newer equipment (156.90 kg.ha-1). The field losses were attributed primarily to base cutting operations (36%) and roller mechanisms (34%), collectively accounting for 70% of the total losses. The contamination analysis revealed sugarcane tops as the predominant impurity source (57% The revenue loss analysis indicates excessive field losses from ageing equipment reducing the farm profitability by 12-18%. The non-linear relationship between the equipment age and performance demonstrates that maintenance practices significantly influence degradation patterns, providing critical insights for optimising mechanical harvesting systems.
The non-destructive estimation of the corn kernel moisture content is essential for determining the optimal harvest period. Although various spectrometer sensors are currently available, their predictive performance differs due to variations in the spectral resolution and wavelength coverage. This study compared the performance of several portable spectrometer sensors with different wavelength ranges for predicting the corn moisture content. Spectral data and reference moisture content were used to develop the prediction models using partial least squares regression (PLSR) and an artificial neural network (ANN). Based on the PLSR modelling, the AS7265X and C12880MA sensors produced the best performance, with coefficients of determination (R2) for training and testing reaching up to 0.90. Furthermore, the ANN modelling yielded improved predictive accuracy, with the highest R2 value of 0.95 obtained using the same sensor combination. These results demonstrate that portable spectrometers show strong potential for the non-destructive field-based prediction of the corn moisture content and can serve as a reliable indicator for determining the optimal harvest timing.
The objective of this study was to evaluate the potential for the sustainable reuse and the value of the residual nut mixture (RNM) by-products (cashew nut, peanut, and soybean) after extraction. To investigate the drying kinetics, the RNM was dried at various temperatures (50 to 80 °C). The Balbay and Şahin model, which had a high coefficient of determination (R2) of 99.62-99.96%, a low root mean square error of 0.007-0.021, and χ2 of 0.001-0.005, was the one that best fit the experimental data out of the eight mathematical models that were used. Artificial neural networks showed higher and faster prediction capacity than the mathematical models. The effective moisture diffusion coefficient (Deff) increased gradually with the temperature, with an activation energy (Ea) of 13.67 kJ∙mol-1. The RNM powder produced by the optimal drying process (60 °C for 3.75 h) has a bright colour, high polyphenol content (2.68 mg gallic acid equivalent (GAE)∙g-1) and antioxidant activity, low moisture content (4.9%) and relatively high nutritional value, especially protein (27.27%), lipid (40.19%), and fibre (4.2%). Under these conditions, not only is efficient drying and preservation achieved, but the quality of the by-product powder is also maintained.
The study assessed the mechanical and colorimetric properties of cocoa (Theobroma cacao L.) pods at various maturity stages to improve the mechanised pod-opening efficiency. Thirty pods, each replicated three times, underwent compressive loading and colour analysis using the CIE Lab* colour space to correlate the colour attributes with mechanical resistance. The pod maturity significantly (P < 0.05) influenced the opening force and compressive strength: unripe pods required the highest mean force (1 222 N) and strength (0.316 N.mm-2), while aged pods needed only 346 N and 0.094 N.mm-2. The longitudinal orientation yielded higher and more consistent force response than the transverse orientation, with the ANOVA confirming significant differences (P < 0.05). The colour parameters, especially yellowness (b*) and hue angle (H°), were strongly and negatively correlated with the mechanical properties (r ≥ -0.99, P ≤ 0.05), making them reliable non-destructive indicators of maturity. The Partial Least Squares Regression (PLSR) models validated the predictive power of the combined colour and mechanical data, with the longitudinal orientation producing the highest model accuracy across all the maturity levels. Combining the colour-based maturity assessment with mechanical testing provides a robust framework for designing automated, maturity-sensitive cocoa pod-opening systems that optimise the efficiency and reduce bean damage during post-harvest handling. The research approach provided an outstanding quantitative assessment.
Octopus (Octopus sp.) is highly perishable marine species for which efficient drying is essential to extend shelf life in tropical climates. The anatomical heterogeneity of the octopus complicates consistent drying. This study systematically evaluated the performance of a mixed solar dryer (MSD) and open solar drying (OSD) across distinct anatomical regions (head, mantle, and tentacles), with emphasis on drying kinetics and quality attributes. Five thin-layer models were applied to characterize moisture reduction, and product quality was assessed by measuring browning, protein, fat, and ash content. The MSD achieved a 20% higher temperature and 29% lower humidity, resulting in a 74% increase in drying rate relative to OSD. The Hasibuan and Daud model exhibited the highest predictive accuracy (coefficient of determination (R2) = 0.9965; root mean square error (RMSE) = 0.0168; sum of squared errors (SSE) = 0.0058). Significant interaction effects between anatomical region and drying method were observed for browning and ash content (P < 0.05), whereas protein and fat content were primarily influenced by anatomical characteristics. Overall, the MSD produced products with reduced browning and enhanced nutrient retention. These results support the implementation of MSD technology by small-scale processors to improve both drying efficiency and product quality in octopus preservation.
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 a completely randomised block design with three replications. The results showed that T2 had higher growth parameters, while T4 and T6 performed poorly. The yield was significantly higher in T2 (23.8 t/ha in 2023 and 23.3 t/ha in 2024), whereas T6 had a lower yield (9.5 t/ha in 2023 and 8.6 t/ha in 2024). Higher water productivity was observed in T3 (9.85 kg/m3 in 2023 and 8.35 kg/m3 in 2024), while T6 had lower water productivity (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 optimise the water productivity and maximise the yield in the greenhouse-grown okra, respectively.
This study investigated the drying kinetics of Rosa flower buds (Rosa damascene Mill) under environmental conditions (shade), as well as in direct and indirect solar dryers. The effects of 40 °C, 50 °C, and 60 °C temperatures on the drying of the buds were also examined using a laboratory cabinet dryer. The drying rate of the Rosa flower buds was compared with various mathematical models, and the parameters of these models were evaluated. The results illustrated that drying Rosa flower buds under shade required a lengthy period time of approximately 13 days. In contrast, utilising solar dryers significantly reduced the drying period time for Rosa flower buds. Also, compared to the ambient drying method, the use of indirect solar dryers had the most substantial effect on decreasing the drying period time of the buds up to 86.6%. Furthermore, applying a temperature of 60 °C in the laboratory dryer reduced the drying time of the buds by 76.2% compared to a temperature of 40 °C. The Midilli et al. model (MDM), Page model (PM) and approximate diffusion model (ADM) demonstrated a good fit with the experimental data and can be employed to represent the drying behaviour of Rosa flower buds. The effective of moisture diffusivity of Rosa flower buds during drying was found to be in a range from 6.87 × 10-12 to 1.89 × 10-10 m2.s-1 and the activation energy values were determined as 65.30 and 72.80 kJ∙mol-1 for buds which were dried in the laboratory cabinet dryer and those dried using the ambient and solar dryer methods, respectively.
The study aimed to optimise 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 optimisation 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 h 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 colour. The microstructure of the foam-mat dried PFSP powder (via SEM images) was also observed.
This paper presents the architecture of a cyber-physical system for the automated washing of agricultural machinery, designed to enhance efficiency and intelligent control. The system includes four layers - physical, sensor, computational, and interface and integrates actuators, sensors, decision-making modules, and analytics. A Python-based simulation using Control and SimPy showed an average washing time of 10.4 minutes and 97.5% cycle initiation accuracy under critical contamination. The Control was achieved via gated recurrent unit (GRU) prediction and proportional-integral-derivative (PID) regulation. Despite assumptions like ideal sensors and fixed conditions, the system proved feasible, with the future work targeting real-world validation and digital twin development.
The productivity and quality of the entire flock are negatively impacted by heat stress in chickens, which can have major repercussions, particularly in crowded farming settings where diseases are easy to spread and hard to control. This study uses deep networks and optical flow to identify heat stress in chickens. The technique focuses on identifying obvious signs of heat stress, such as panting and open-mouth breathing in chickens. There are two phases to the suggested approach: (1) using a deep network to detect open-mouth breathing in chickens; (2) using the Gunnar Farnebäck algorithm to compute the optical flow vectors of the wattle, the breathing frequency is estimated in the Fourier domain for the detection of panting chickens. The proposed method was tested on the obtained dataset and demonstrated its ability to recognise heat-stressed chickens in crowded conditions, achieving an overall performance metric of 0.90 by integrating the results of both phases. The two-phase approach, which incorporates the open-mouth breathing behaviour and panting frequency, improves the efficiency and assures robust, reliable heat stress detection.
This study explores the construction of small reservoirs (SRs) as a strategic solution to address water scarcity in Kashkadarya Province, Uzbekistan, where agricultural productivity is heavily dependent on irrigation. By utilising geographic information system (GIS) and remote sensing (RS) technologies, optimal locations for reservoirs were identified, focusing on improving the water availability for irrigation during critical periods. The research highlights the socio-economic and environmental benefits of SRs, including enhanced agricultural yields, increased employment opportunities, and reduced reliance on energy-intensive pumping stations. The findings indicate that the construction of an 18 Mm3 reservoir in the Ayakchisoy River could supply water to 26.5 thousand hectares, thereby improving the region's resilience to climate variability. This approach offers a sustainable framework for managing water resources in arid regions, contributing to food security and economic stability.
This study optimised the drying process of kaffir lime leaves (Citrus hystrix DC.) to extend the shelf life and preserve the quality by reducing the moisture content. A Central Composite Design (CCD) under the Response Surface Methodology (RSM) framework was employed, with the temperature (55 °C, 60 °C, 65 °C) and drying time (5, 5.5, 6 h) as the variables. Experiments were conducted with a mechanical food dehydrator, and the parameters evaluated included the water content and drying yields. The optimal condition was identified as 60 °C for 5 h, resulting in a drying yield of 33.3% and a final moisture content of 7.153 (% w.b.), which meets the quality standards for dried products. The novelty of this research lies in the application of RSM to determine effective drying conditions for kaffir lime leaves, which are not widely used, but have good economic potential. The research proved that drying with the right conditions can greatly improve the quality and stability of dried kaffir lime leaves.
Indonesia, as a leading coconut producer, generates a substantial amount of coconut pulp from coconut milk extraction, which can be further processed into low-fat desiccated coconut. However, the drying process induces browning, reducing its whiteness and overall quality. Sodium metabisulphite is commonly used to prevent browning, but excessive use raises health concerns. In this study, the optimisation of the drying temperature and sodium metabisulphite concentration was conducted to minimise the browning while maintaining the quality of low-fat desiccated coconut. Using Response Surface Methodology (RSM) with a Central Composite Design (CCD), responses such as the browning index (BI), whiteness index (WI), moisture content, yield, free fatty acids (FFAs), ash, fat, protein, total phenolic content (TPC), and crude fibre were examined. The results showed that both the drying temperature and sodium metabisulphite concentration significantly influenced the physicochemical properties. The optimal conditions were identified at 62.505 °C and 380.059 ppm sodium metabisulphite, resulting in a whiteness index of 87.219, browning index of 5.1025, yield of 43.125%, moisture content of 2.3%, and free fatty acid content of 4.45%. These findings highlight an effective strategy for reducing the additive dependency while maintaining the physicochemical quality of low-fat desiccated coconut.
A free electricity tandem-twin-hybrid-solar-biomass dryer comprised of two drying rooms and operated with solar and biomass energy combustion of 10 kg rubber wood per hour separately to dry Robusta coffee cherries with 3, 6, 9, and 12 cm bed thicknesses were studied with the drying completion time (tc), number of defects (ND), and colour parameters, i.e., lightness (L*), hue angle [H(o)], and chroma (C), used as the performance indicators. The experimental results indicated that the drying room, bed thickness, and drying room-bed thickness interaction significantly affected the tc and ND and bed thickness only significantly affected C for both the solar energy drying and the biomass energy drying. The solar energy drying generated a drying air temperature of 44.6 ± 3.5 °C with a tc of 70.9-90.2 h for the front drying room and 40.1 ± 2.8 °C with a tc of 77.2-116.5 h for the rear drying room, whereas the biomass energy drying produced a drying air temperature of 57.2 ± 3.6 °C with a tc of 34.1-44.9 h for the front drying room and 45.6 ± 6.0 °C with a tc of 56.3-96.6 h for the rear drying room. Both drying processes produced coffee beans with the NDs less than 11 qualified for Grade 1 with similar colour characteristics.
Banana quality is influenced by many factors, including variety and level of fruit ripeness. This quality can be evaluated from various points of view, one of which is the rheological consideration. Rheological properties are very important to study because they determine the design of equipment and processes, and minimise product damage. The aim of this research was to analyse and model the effect of variety and ripeness level on the rheological properties of banana fruit by applying a creep test. This research was carried out using a factorial experimental design 3 × 3 with 5 replications. Three varieties of banana, namely Ambon (Musa parasidiaca var. sapientum L. Kunt.), Raja (Musa parasidiaca L.), and Kepok (Musa acuminata balbisiana Colla) and each in three levels of ripeness, namely mature green, half ripe, and ripe. It was found that the parameters of the rheological properties of bananas changed according to the ripeness level (P < 0.05). The values of these rheological parameters decreased as the bananas ripened. Meanwhile, the variety and the interaction between variety and ripeness had no significant influence on the rheological parameters measured (P > 0.05). The constants of the four-element Burger model changed with the ripeness in all banana varieties. The Burger model with four elements could accurately predict the strain value of bananas tested in the creep test.
Rice fields are the main source of rice production. Rice field expansion is one way to increase rice production. Rice field expansion activities for the upland area in Indonesia are often overlooked due to limited information about the availability of suitable land. In upland areas, rice terraces are often found. Until now, there has been no guideline for determining the suitable location of rice terraces on upland areas. The purpose of this study was to develop a land suitability assessment model for rice terraces using geographic information system (GIS) and the analytical hierarchical process (AHP) on upland areas like the Lake Toba catchment area. There were four important factors for selecting rice terracing plantations: spatial planning, slope, texture, and distance from the river (hydrology). By using the AHP method, a rating has been assigned to each theme layer. To create the suitability map for rice terraces in a GIS setting, all the thematic layers were combined into a single layer using the weighted overlay approach. The results showed that 37.78% were highly suitable, 18.88% were moderately suitable, and 36.95% were marginally suitable for rice terraces. The model can be used to determine the location of rice terraces on upland areas with a high accuracy of about 93%.
Moisture content regulation of root crops is crucial in post-harvest processing operations, not only in the price stipulation but also to avoid aflatoxin contamination. To prolong their storage life, they are processed into dried chips to extend their usability in feed formulations and starches. In this study, we use the capacitance-based method to evaluate the performance of an analogue-based electronic meter for the cassava, sweet potato, and taro chips. The meter was calibrated against the oven-drying method, yielding high R2 values of the different root crops. The established calibration models were validated and revealed high R2 values with 0.9580 for the cassava, 0.9958 for the sweet potato, and 0.9798 for the taro. The trendline equations are y = 59.44x0.56, y = 54.38x0.47, and y = 52.94x0.62, respectively. The results revealed that the moisture meter is capable of reading the moisture content on a weight basis (% MCwb) with accuracy and reliability at specified limits of 8% < x < 69% for the cassava, 15% < x < 59% for the sweet potato, and 9% < x < 57% for the taro. This study presents the performance of a portable analogue-based moisture meter as a reliable and accessible solution to small-scale operations, especially for farmers, offering an on-site rapid moisture content measurement in root crop processing.
Soil water content is critical in plants' morphological and physiological processes; therefore, water must always be available in appropriate quantities to meet plant growth needs. Soil moisture can be easily detected using sensors, which offer a practical solution for monitoring water content in the soil. However, using sensors for a long time, especially on agricultural land, will reduce sensor accuracy. This research aims to investigate the accuracy of soil moisture sensors during their use for cultivating crops. Using sensors in sandy clay soil can detect soil moisture levels with an accuracy of 93.80% and a precision of 90.81%. A reading deviation (error) of up to 49.74% with a precision level of 75.69% occurred when the sensor had been used for 40 days. Regular cleaning and calibration of the sensor are necessary to obtain accurate soil moisture readings. A copper-based sensor module kit can be used to detect soil moisture with reasonable accuracy during plant growth with a 5-6 weeks harvest time.
With the increasing productivity in agriculture, it has become extremely essential to look for an advanced technique that will help to minimise losses. Recently, deep learning has outperformed the task of recognition and classification of fruits and vegetables automatically from images, finding applicability in this study. This work, thus, attempts to develop an automatic spoilage detection CNN model for tomatoes. In this work, a deep learning-based CNN model is trained and validated on a self-prepared dataset for classifying tomatoes as edible and spoilt is proposed. The dataset consisted of 810 images, out of which 572 images were considered for training and 238 images for validation. The model is also trained iteratively with varying epoch and batch sizes to evaluate the model in giving the highest classification accuracy. The highest accuracy of 99.70% was achieved at epoch 20 and batch size 32. Further evaluating the performance of the developed model using a confusion matrix, a precision, recall and accuracy of 100%, 87% and 95%, respectively, was obtained for the spoilage detection of tomatoes. Also, on establishing Pearson's correlation between the predictive model and the sensory evaluation results, a Pearson correlation of 0.895 was obtained, showing that there is strong linear correlation between them.
