Search Results (17)

The prolonged use of traditional polyethylene mulch (PM) films has resulted in significant environmental issues, such as soil residues and white pollution, which pose challenges to sustainable agriculture. The transition from PM to fully biodegradable mulch (BDM) films has emerged as a prominent trend in contemporary farming practices. This study investigates the effects of various colors of biodegradable mulches on watermelon production and quality, with a particular emphasis on BDM in comparison to conventional PM. Within the 0.2–15.3 µm wavelength range, transparent variants demonstrate high light transmission, while the silver–black treatment exhibits greater reflectivity. The silver–black surface effectively reduces evaporation, maintaining soil water content 5–8% higher than that of PM. However, its thermal profile reveals periodic temperature increases similar to those observed with PM. The results indicate that BDM silver–black enhances biomass, root N and P levels, and leaf NPK retention compared to PM. Notably, among the BDM treatments, silver–black yielded the highest average fruit weight and width (7.68 kg, 22.83 cm), although these differences were not statistically significant when compared to PM. Additionally, it produced the highest soluble solids content (13.2 °Brix) at a significance level of p < 0.05 relative to PM. This finding suggests an enhancement in the soil’s capacity to retain moisture and its nutrient availability, thereby fostering plant growth. All treatments proved profitable and economically viable; however, the total inputs and outputs associated with BDM silver–black and CK-PM transparent yielded a satisfactory profit, ranging from $1937 to $2503 per hectare. These results advocate for the utilization of sensor-embedded mulch films and the silver–black color to optimize water and nutrient utilization, thereby promoting sustainable watermelon cultivation. Full article

Unmanned aerial vehicles (UAVs) with multispectral sensors are transforming precision viticulture by enabling detailed monitoring of vineyard variability. Vegetation indices such as NDVI, NDRE, GNDVI, and SAVI are widely applied to estimate vine vigor, canopy structure, and water status. Beyond agronomic traits, UAV-derived indices can inform grape composition, including sugar content (°Brix), total phenolics, anthocyanins, titratable acidity, berry weight, and yield variables measurable in the field or laboratory to validate spectral predictions. Strengths of UAV approaches include high spatial resolution, rapid data acquisition, and flexibility across vineyard blocks, while limitations involve index saturation in dense canopies (e.g., Merlot, Cabernet Sauvignon), environmental sensitivity, and calibration requirements across varieties and phenological cycles. Integrating UAV data with ground-based measurements (leaf sampling, yield mapping, proximal or thermal sensors) improves model accuracy and stress detection. Abiotic stresses (water deficit, nutrient deficiency) can be distinguished from biotic factors (pest and fungal infections), supporting timely interventions. Compared to manned aircraft or satellite platforms, UAVs offer cost-effective, high-resolution imagery for precision vineyard management. Future directions include combining UAV indices with machine learning and data fusion to predict grape maturity and wine quality, enhancing decision-making in sustainable viticulture and precision enology. Full article

Osmotic dehydration (OD) is an effective pre-treatment for fruit preservation, but conventional processes often lack precision due to manual control of critical variables. This work reports the design and validation of an automated OD system integrating a programmable logic controller (PLC), human–machine interface (HMI), and IoT-enabled sensors for real-time monitoring of syrup concentration and process temperature. Mango (Mangifera indica) cubes were treated under a 2³ factorial design with sucrose concentrations of 45 and 50 °Brix, immersion times of 120 and 180 min, and temperatures of 30 and 40 °C. Validation demonstrated that the IoT hydrometer achieved strong agreement with reference devices (R² = 0.985, RMSE = 0.36 °Brix), while the PLC-integrated tank sensor also demonstrate improved performance over existing calibrated thermometer (R² = 0.992, MAE = 0.20 °C). ANOVA indicated that concentration, temperature, and time significantly affected water loss and weight reduction (p < 0.01), with temperature being the dominant factor. Water loss ranged from 18.62% to 39.15% and weight reduction from 9.48% to 34.47%, while maximum solid gain reached 9.31% at 50 °Brix and 40 °C for 180 min, with stabilization consistent with case hardening. Drying kinetics were best described by the Page model (R² > 0.97). The findings highlight the effectiveness of the system for precise monitoring and optimization of OD processes. 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

In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers. Full article

Apples are widely cultivated in the Republic of Korea and are preferred by consumers for their sweetness. Soluble solid content (SSC) is measured non-destructively using near-infrared (NIR) spectroscopy; however, the SSC measurement error increases with the change in apple size since the distance between the light source and the near-infrared sensor is fixed. In this study, spectral characteristics caused by the differences in apple size were investigated. An optimal SSC prediction model applying partial least squares regression (PLSR) to three measurement conditions based on apple size was developed. The three optimal measurement conditions under which the Vis/NIR spectrum is less affected by six apple size levels (Levels I–VI) were selected. The distance from the apple center to the light source and that to the sensor were 125 and 75 mm (Distance 1), 123 and 75 mm (Distance 2), and 135 and 80 mm (Distance 3). The PLSR model applying multiplicative scatter correction pretreatment under Distance 3 measurement conditions showed the best performance for Level IV-sized apples (${\mathrm{R}}_{\mathrm{p}\mathrm{r}\mathrm{e}}^2$ = 0.91, RMSEP = 0.508 °Brix). This study shows the possibility of improving the SSC prediction performance of apples by adjusting the distance between the light source and the NIR sensor according to fruit size. Full article

The digitalization of information is crucial for the upgrading of the bayberry digital agriculture industry, while the low-cost information detection sensing equipment for bayberry are a bottleneck for the digital development of the industry. The existing rapid and non-destructive detection devices for fruit acidity and sugar content mainly use near-infrared and mid-infrared spectral characteristic for detection. These devices use expensive InGaAs sensor, which are difficult to promote and apply in the bayberry digital industry. This study is based on the high-spectral range of 454–998 nm in bayberry fruit to study the mechanism of fruit sugar and acidity content detection and to develop a portable bayberry fruit sugar and acidity detection device using Si-sensor in order to achieve low-cost quality parameter detection of bayberry fruit. The research results show that: Based on the hyperspectral of bayberry fruit, the sensitive wavelength for sugar content inversion is 610 nm, and the inversion accuracy (RMSE) is 1.399Brix; the sensitive wavelength for pH inversion is 570 nm, and the inversion accuracy (RMSE) is 0.1329. Based on the above spectroscopic detection mechanism and spectral dimension reduction methods, combined with low-cost Si-sensor (400–1000 nm), a low-cost non-destructive portable bayberry fruit sugar and acidity detection device has been developed, with detection accuracies of 94.74% and 97.14%, respectively. This bayberry fruit sugar and acidity detector provides a low-cost portable non-destructive quality detection instrument of bayberry, which is in line with the industrial group of low consumption in which the bayberry is mainly cultivated on a small scale, accelerating the digitalization process of the bayberry industry. Full article

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (EC_a) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R²) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R² of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R² of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way. Full article

Sustainable and efficient agricultural production is a growing priority in modern society. Viticulture, an important agricultural and food sector, also faces this challenge. Precision Viticulture (PV) has gained prominence as it aims to foster high-quality, efficient, and environmentally sustainable practices. The Soluble Solids Content (SSC) is essential for assessing grape ripeness and quality in the winemaking process. Conventional methods for determining SSC values (expressed in °Brix) are invasive, expensive, and labour-intensive, necessitating sample preparation, making large-scale analysis impractical. In response to these limitations, this study presents an innovative approach within the field of Precision Viticulture. It focuses on the non-invasive prediction of SSC using low-cost proximal hyperspectral optical sensors. These sensors rely on spectral reflectance measurements in the range of 340–850 nm. This study was conducted in a commercial vineyard in the Demarcated Douro Region, Cima-Corgo sub-region, Portugal, over six weeks during ripening. In total, 169 grape berries from Touriga Nacional vines were analysed under three irrigation regimes (no irrigation, 30% ETc, and 60% ETc). After organising and preprocessing the data, machine learning algorithms, namely Partial Least Squares Regression (PLS), Random Forest (RF), and the Generalised Linear Model (GLM), were applied to predict SSC values. These models’ performance was thoroughly evaluated using cross-validation techniques. The performance of different models was evaluated, showing significant differences according to the metrics used (R2, RMSE, and MAPE). The RF model demonstrated effectiveness and precision. A high R² value of 0.9312, coupled with low RMSE (0.9199 °Brix) and MAPE (3.88%), signifies a strong fit to the data and accurate predictive capabilities. The results of this benchmarking study on predictive models of SSC provide valuable insights into the performance of various models, aiding winegrowers and winemakers in decision making. Full article

This paper describes a Fabry–Pérot sensor-based measuring system for measuring fluid composition in demanding industrial applications. The design of the sensor is based on a two-parametric sensor, which enables the simultaneous measurement of temperature and refractive index (RI). The system was tested under real industrial conditions, and enables temperature-compensated online measurement of emulsion concentration with a high resolution of 0.03 Brix. The measuring system was equipped with filtering of the emulsion and automatic cleaning of the sensor, which proved to be essential for successful implementation of a fiber optic RI sensor in machining emulsion monitoring applications. Full article

This paper presents a Brix sensor based on the differential pressure measurement principle. Two piezoresistive silicon pressure sensors were applied to measure the specific gravity of the liquid, which was used to calculate the Brix level. The pressure sensors were mounted inside custom-built water-tight housings connected together by fixed length metallic tubes containing the power and signal cables. Two designs of the sensor were prepared; one for the basic laboratory testing and validation of the proposed system and the other for a fermentation experiment. For lab tests, a sugar solution with different Brix levels was used and readings from the proposed sensor were compared with a commercially available hydrometer called Tilt. During the fermentation experiments, fermentation was carried out in a 1000 L tank over 7 days and data was recorded and analysed. In the lab experiments, a good linear relationship between the sugar content and the corresponding Brix levels was observed. In the fermentation experiment, the sensor performed as expected but some problems such as residue build up were encountered. Overall, the proposed sensing solution carries a great potential for continuous monitoring of the Brix level in liquids. Due to the usage of low-cost pressure sensors and the interface electronics, the cost of the system is considered suitable for large scale deployment at wineries or juice processing industries. Full article

Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis of the agricultural products and alcoholic beverages. The Brix monitoring of fruit and vegetables by destructive methods includes sensory assessment involving sensory panels, instruments such as refractometer, hydrometer, and liquid chromatography. However, these techniques are manual, time-consuming, and most importantly, the fruits or vegetables are damaged during testing. On the other hand, the traditional sample-based methods involve manual sample collection of the liquid from the tank in fruit/vegetable juice making and in wineries or breweries. Labour ineffectiveness can be a significant drawback of such methods. This review presents recent developments in different destructive and nondestructive Brix measurement techniques focused on fruits, vegetables, and beverages. It is concluded that while there exist a variety of methods and instruments for Brix measurement, traits such as promptness and low cost of analysis, minimal sample preparation, and environmental friendliness are still among the prime requirements of the industry. Full article

We propose two new sensors based on surface plasmon resonance (SPR) and optical fibers to determine the concentration of sucrose in products such as beverages, honey, condensed milk, etc., in real-time during the fabrication process or when the product has been manufactured. The sensors have been made with a hemispherical prism, a layer of MgF₂, and another of Ag or Al with the Kretschmann configuration, and they are modulated in intensity. We have optimized these sensors from the modeling of reflectance curves. We have carried out a numerical simulation with these sensors to show how they can detect small changes in the refractive index depending on the concentration of sucrose where the device is immersed. The maximum sensitivity of the sensors is 11.9 RIU⁻¹ and 5.7 RIU⁻¹, the resolutions 1.7 × 10⁻⁴ RIU and 7.9 × 10⁻⁴ RIU, and the detection limits between 0-78Brix. Moreover, the sensors have an alarm system that is triggered when the sucrose concentration is insufficient or excessive. Data can also be sent in real-time to a remote place. Full article

Farm-to-table operations produce, transport, and deliver produce to consumers in very different ways than conventional, corporate-scale agriculture operations. As a result, the time it takes to get a freshly picked fruit to the consumer is relatively short and the expectations of the consumer for freshness and quality are high. Since many of these operations involve small farms and small businesses, resources to deploy sensors and instruments for monitoring quality are scarce compared to larger operations. Within stringent power, cost, and size constraints, this article analyzes chemical sensor technologies suitable for monitoring fruit quality from the point of harvest to consumption in farm-to-table operations. Approaches to measuring sweetness (sugar content), acidity (pH), and ethylene gas are emphasized. Not surprisingly, many instruments developed for laboratory use or larger-scale operations are not suitable for farm-to-table operations. However, there are many opportunities still available to adapt pH, sugar, and ethylene sensing to the unique needs of localized farm-to-table operations that can help these operations survive and expand well into the future. Full article

Agrivoltaic (agriculture–photovoltaic) or solar sharing has gained growing recognition as a promising means of integrating agriculture and solar-energy harvesting. Although this field offers great potential, data on the impact on crop growth and development are insufficient. As such, this study examines the impact of agriculture–photovoltaic farming on crops using energy information and communications technology (ICT). The researched crops were grapes, cultivated land was divided into six sections, photovoltaic panels were installed in three test areas, and not installed in the other three. A 1300 × 520 mm photovoltaic module was installed on a screen that was designed with a shading rate of 30%. In addition, to collect farming-cultivation-environment data and to analyze power generation, sensors for growing environments and wireless-communication devices were used. As a result, normal modules generated 25.2 MWh, bifacial modules generated 21.6 MWh, and transparent modules generated 25.7 MWh over a five-month period. We could not find a difference in grape growth according to the difference of each module. However, a slight slowing of grape growth was found in the experiment group compared to the control group. Nevertheless, the sugar content of the test area of the grape fruit in the harvest season was 17.6 Brix on average, and the sugar content of the control area was measured at 17.2 Brix. Grape sugar-content level was shown to be at almost the same level as that in the control group by delaying the harvest time by about 10 days. In conclusion, this study shows that it is possible to produce renewable energy without any meaningful negative impact on normal grape farming. Full article