In this study, a low-cost, self-balancing crop spectral reflectance sensor (CSRS) was designed for real-time, nondestructive monitoring of the spectral reflectance and vegetation index of crops such as tomato and rapeseed. The sensor had a field of view of 30°, and a narrow-band filter was used for light splitting. The filter’s full width at half-maximum was 10 nm, and the spectral bands were 710 nm and 870 nm. The sensor was powered by a battery and used WiFi for communication. Its software was based on the Contiki operating system. To make the sensor work in different light intensity conditions, the photoelectric conversion automatic gain circuit had a total of 255 combinations of amplification. The gimbal of the sensor was mainly composed of an inner ring and an outer ring. Under the gravity of the sensor, the central axis of the sensor remained vertical, such that the up-facing and down-facing photosensitive units stayed in the horizontal position. The mechanical components of the sensor were designed symmetrically to facilitate equal mass distribution and to meet the needs of automatic balancing. Based on the optical signal transmission process of the sensor and the dark-current characteristics of the photodetector, a calibration method was theoretically deduced, which improved the accuracy and stability of the sensor under different ambient light intensities. The calibration method is also applicable for the calibration of other crop growth information sensors. Next, the standard reflectance gray scale was taken as the measurement variable to test the accuracy of the sensor, and the results showed that the root mean square error of the reflectance measured by the sensor at 710 nm and 870 nm was 1.10% and 1.27%, respectively; the mean absolute error was 0.95% and 0.89%, respectively; the relative error was below 4% and 3%, respectively; and the coefficient of variation was between 1.0% and 2.5%. The reflectance data measured by the sensor under different ambient light intensities suggested that the absolute error of the sensor was within ±0.5%, and the coefficients of variation at the two spectral bands were 1.04% and 0.39%, respectively. With tomato and rapeseed as the monitoring targets, the proposed CSRS and a commercial spectroradiometer were used to measure at the same time. The results showed that the reflectance measured by the two devices was very close, and there was a linear relationship between the normalized difference vegetation index of the CSRS and that of the commercial spectroradiometer. The coefficient of determination (R²) for tomato and rapeseed were 0.9540 and 0.9110, respectively.

Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change.

Macrolide pollution has attracted a great deal of attention because of its ecotoxic effects on microalgae, but the role of phycospheric bacteria under antibiotic stress remains unclear. This study explored the toxic effects of erythromycin (ERY) on the growth and nitrogen metabolism of Auxenochlorella pyrenoidosa; then, it analyzed and predicted the effects of the composition and ecological function of phycospheric bacteria on microalgae under ERY stress. We found that 0.1, 1.0, and 10 mg/L ERY inhibited the growth and chlorophyll of microalgae, but the microalgae gradually showed enhanced growth abilities over the course of 21 days. As the exposure time progressed, the nitrate reductase activities of the microalgae gradually increased, but remained significantly lower than that of the control group at 21 d. NO₃⁻ concentrations in all treatment groups decreased gradually and were consistent with microalgae growth. NO₂⁻ concentrations in the three treatment groups were lower than those in the control group during ERY exposure over 21 d. ERY changed the community composition and diversity of phycospheric bacteria. The relative abundance of bacteria, such as unclassified-f-Rhizobiaceae, Mesorhizobium, Sphingopyxis, Aquimonas, and Blastomonas, varied to different degrees. Metabolic functions, such ABC transporters, the microbial metabolism in diverse environments, and the biosynthesis of amino acids, were significantly upregulated in the treatments of higher concentrations (1.0 and 10 mg/L). Higher concentrations of ERY significantly inhibited nitrate denitrification, nitrous oxide denitrification, nitrite denitrification, and nitrite and nitrate respiration. The findings of this study suggest that phycospheric bacteria alleviate antibiotic stress and restore the growth of microalgae by regulating nitrogen metabolism in the exposure system.

The spectral reflectance provides valuable information regarding vegetation growth and plays an important role in agriculture, forestry, and grassland management. In this study, a small, portable vegetation canopy reflectance (VCR) sensor that can operate throughout the day was developed. The sensor includes two optical bands at 710 nm and 870 nm, with the light separated by filters, and has a field of view of 28°. It is powered by two 14500 rechargeable batteries and uses Wi-Fi for data transmission. The calibration of the sensor was performed using an integrating sphere, and a solar altitude correction model was constructed. The sensor’s accuracy was validated using a standard reflectance gray scale board. The results indicate that the root mean square error (RMSE) and mean absolute error (MAE) at 710 nm were 1.07% and 0.63%, respectively, while those at 870 nm were 0.94% and 0.50%, respectively. Vegetation at 14 sites was measured using both the VCR sensor and an Analytical Spectral Devices (ASD) spectroradiometer at nearly the same time for each site. The results show that the reflectance values measured by both devices were closely aligned. Measurements of Bermuda grass vegetation on clear days revealed that the intra-day reflectance range at 710 nm narrowed from 12.3–19.2% before solar altitude correction to 11.1–13.4% after correction, and the coefficient of variation (CV) decreased from 10.86% to 2.93%. Similarly, at 870 nm, the intra-day reflectance range decreased from 41.6–60.3% to 39.0–42.0%, and the CV decreased from 9.69% to 1.53%. In summary, this study offers a fundamental tool for monitoring vegetation canopy reflectance in the field, which is crucial for advancing high-quality agricultural, grassland, and forest management practices.

Rapid and non-destructive estimation of soluble solids content (SSC) is essential for tomato quality evaluation, yet the generalization ability of many existing spectral models remains limited when applied across multiple cultivars. In this study, hyperspectral reflectance was combined with a genetic algorithm (GA)–based optimization strategy to develop a robust SSC prediction framework applicable to diverse tomato types. Spectral reflectance and SSC (°Brix) were measured for 152 fruits representing 13 cultivars, including large red, medium red, red cherry, and yellow cherry types. To overcome the structural rigidity of conventional fixed-form spectral indices, reflectance spectra were linearly transformed to construct three novel indices: the linearly transformed difference spectral index (ltDSI), linearly transformed normalized difference spectral index (ltNDSI), and linearly transformed ratio spectral index (ltRSI). For each index, GA was employed to simultaneously optimize wavelength combinations and transformation coefficients. Under identical calibration and validation datasets, the GA-optimized indices consistently outperformed conventional two-band spectral indices as well as full-spectrum partial least squares models, while exhibiting markedly reduced sensitivity to tomato type. Across all validation datasets, the proposed models achieved coefficients of determination of approximately 0.80, with root mean square errors around 0.6°Brix and mean relative errors close to 10%. These results demonstrate that joint optimization of spectral index structure and parameters is an effective strategy for improving model robustness and transferability. The proposed framework provides a scalable solution for non-destructive SSC assessment and offers practical guidance for the development of low-cost, field-deployable spectral sensing tools for fruit quality phenotyping across cultivars and growing conditions.

Fluorescence-based sensors provide an approach for estimating cyanobacterial density through phycocyanin detection; however, their accuracy can be affected by ambient light interference and variations in optical path length during in situ deployment. In this study, a relative scaling model was established to describe excitation and fluorescence propagation within a confined water column, enabling quantitative analysis of the influence of measurement depth on sensor output. Based on this analysis, a depth-limiting and light-shielding optical sleeve was designed to maintain a fixed optical path length and reduce external light interference. Calibration experiments using Microcystis aeruginosa were conducted at three controlled water depths inside the sleeve (5, 8, and 11 cm), yielding strong linear relationships with microscopic cell counts (R² > 0.99), with nearly overlapping responses at 8 cm and 11 cm. Under strong midday illumination, the normalized RMSE decreased from 0.123 (without sleeve) to 0.028 with the sleeve. Additional validation using mixed cyanobacterial samples and natural water bodies demonstrated that consistent density estimation can be achieved when communities are dominated by species with comparable cell volumes. The proposed optical sleeve provides a geometric stabilization strategy for reducing depth-related variability and ambient light interference in phycocyanin-based cyanobacterial density estimation.