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Light Detection and Ranging (LiDAR) is a 3D imaging technique, widely used in many applications such as augmented reality, automotive, machine vision, spacecraft navigation and landing. Achieving long-ranges and high-speed, most of all in outdoor applications with strong solar background illumination, are challenging requirements. In the introduction we review different 3D-ranging techniques (stereo-vision, projection with structured light, pulsed-LiDAR, amplitude-modulated continuous-wave LiDAR, frequency-modulated continuous-wave interferometry), illumination schemes (single point and blade scanning, flash-LiDAR) and time-resolved detectors for LiDAR (EM-CCD, I-CCD, APD, SPAD, SiPM). Then, we provide an extensive review of silicon- single photon avalanche diode (SPAD)-based LiDAR detectors (both commercial products and research prototypes) analyzing how each architecture faces the main challenges of LiDAR (i.e., long ranges, centimeter resolution, large field-of-view and high angular resolution, high operation speed, background immunity, eye-safety and multi-camera operation). Recent progresses in 3D stacking technologies provided an important step forward in SPAD array development, allowing to reach smaller pitch, higher pixel count and more complex processing electronics. In the conclusions, we provide some guidelines for the design of next generation SPAD-LiDAR detectors.

The detection of peaks shifts in Raman spectroscopy enables a fingerprint reconstruction to discriminate among molecules with neither labelling nor sample preparation. Time-resolved Raman spectroscopy is an effective technique to reject the strong fluorescence background that profits from the time scale difference in the two responses: Raman photons are scattered almost instantaneously while fluorescence shows a nanoseconds time constant decay. The combination of short laser pulses with time-gated detectors enables the collection of only those photons synchronous with the pulse, thus rejecting fluorescent ones. This review addresses time-gating issues from the sensor standpoint and identifies single photon avalanche diode (SPAD) arrays as the most suitable single-photon detectors to be rapidly and precisely time-gated without bulky, complex, or expensive setups. At first, we discuss the requirements for ideal Raman SPAD arrays, particularly focusing on the design guidelines for optimized on-chip processing electronics. Then we present some existing SPAD-based architectures, featuring specific operation modes which can be usefully exploited for Raman spectroscopy. Finally, we highlight key aspects for future ultrafast Raman platforms and highly integrated sensors capable of undistorted identification of Raman peaks across many pixels.

Chlorophyll content in plant leaves is an essential indicator of the growth condition and the fertilization management effect of naked barley crops. The soil plant analysis development (SPAD) values strongly correlate with leaf chlorophyll contents. Unmanned Aerial Vehicles (UAV) can provide an efficient way to retrieve SPAD values on a relatively large scale with a high temporal resolution. But the UAV mounted with high-cost multispectral or hyperspectral sensors may be a tremendous economic burden for smallholder farmers. To overcome this shortcoming, we investigated the potential of UAV mounted with a commercial digital camera for estimating the SPAD values of naked barley leaves. We related 21 color-based vegetation indices (VIs) calculated from UAV images acquired from two flight heights (6.0 m and 50.0 m above ground level) in four different growth stages with SPAD values. Our results indicated that vegetation extraction and naked barley ears mask could improve the correlation between image-calculated vegetation indices and SPAD values. The VIs of ‘L*,’ ‘b*,’ ‘G − B’ and ‘2G − R − B’ showed significant correlations with SPAD values of naked barley leaves at both flight heights. The validation of the regression model showed that the index of ‘G-B’ could be regarded as the most robust vegetation index for predicting the SPAD values of naked barley leaves for different images and different flight heights. Our study demonstrated that the UAV mounted with a commercial camera has great potentiality in retrieving SPAD values of naked barley leaves under unstable photography conditions. It is significant for farmers to take advantage of the cheap measurement system to monitor crops.

Time-gating of single-photon avalanche diodes (SPADs) was commonly used as a method to reduce dark noise in biomedical imaging applications where photon events are correlated with a reference clock. Time-gating was also used to obtain timing information of photon events by shifting the gate windows applied to a SPAD array. However, in this approach, fine timing resolution comes at the cost of a lengthened measurement time due to the large number of counts required for each shift. As a solution, we present a multi-time-gated SPAD array that simultaneously applies shifted gate windows to an array of SPADs, which has the potential to reduce the measurement time compared to a single time gate window. Compared to similar works, this design has fully integrated the multi-gate generation using shared circuitry which also functions as a coarse time-to-digital converter. The proposed array, fabricated in the TSMC 65 nm standard CMOS process, achieved a median dark count rate (DCR) of 37 kHz, 4.37 ns gate widths, 550 ps timing resolution, and a peak photon detection probability (PDP) of 42.9% at 420 nm, all at a 0.8 V excess bias.

Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R2 = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R2 = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.

To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.

Low-light imaging capabilities are in urgent demand in many fields, such as security surveillance, night-time autonomous driving, wilderness rescue, and environmental monitoring. The excellent performance of SPAD devices gives them significant potential for applications in low-light imaging. This article presents a 64 (rows) × 128 (columns) SPAD image sensor designed for low-light imaging. The chip utilizes a three-dimensional stacking architecture and microlens technology, combined with compact gated pixel circuits designed with thick-gate MOS transistors, which further enhance the SPAD’s photosensitivity. The configurable digital control circuit allows for the adjustment of exposure time, enabling the sensor to adapt to different lighting conditions. The chip exhibits very low dark noise levels, with an average DCR of 41.5 cps at 2.4 V excess bias voltage. Additionally, it employs a denoising algorithm specifically developed for the SPAD image sensor, achieving two-dimensional grayscale imaging under 6 × 10−4 lux illumination conditions, demonstrating excellent low-light imaging capabilities. The chip designed in this paper fully leverages the performance advantages of SPAD image sensors and holds promise for applications in various fields requiring low-light imaging capabilities.

The aim of this study was to evaluate the biostimulant action (hormone like activity, nitrogen uptake, and growth stimulation) of a plant-derived protein hydrolysate by means of two laboratory bioassays: a corn (Zea mays L.) coleoptile elongation rate test (Experiment 1), a rooting test on tomato cuttings (Experiment 2); and two greenhouse experiments: a dwarf pea (Pisum sativum L.) growth test (Experiment 3), and a tomato (Solanum lycopersicum L.) nitrogen uptake trial (Experiment 4). Protein hydrolysate treatments of corn caused an increase in coleoptile elongation rate when compared to the control, in a dose-dependent fashion, with no significant differences between the concentrations 0.75, 1.5, and 3.0 ml/L, and inodole-3-acetic acid treatment. The auxin-like effect of the protein hydrolysate on corn has been also observed in the rooting experiment of tomato cuttings. The shoot, root dry weight, root length, and root area were significantly higher by 21, 35, 24, and 26%, respectively, in tomato treated plants with the protein hydrolysate at 6 ml/L than untreated plants. In Experiment 3, the application of the protein hydrolysate at all doses (0.375, 0.75, 1.5, and 3.0 ml/L) significantly increased the shoot length of the gibberellin-deficient dwarf pea plants by an average value of 33% in comparison with the control treatment. Increasing the concentration of the protein hydrolysate from 0 to 10 ml/L increased the total dry biomass, SPAD index, and leaf nitrogen content by 20.5, 15, and 21.5%, respectively. Thus the application of plant-derived protein hydrolysate containing amino acids and small peptides elicited a hormone-like activity, enhanced nitrogen uptake and consequently crop performances.

We present a Montecarlo simulator developed in Matlab® for the analysis of a Single Photon Avalanche Diode (SPAD)-based Complementary Metal-Oxide Semiconductor (CMOS) flash Light Detection and Ranging (LIDAR) system. The simulation environment has been developed to accurately model the components of a flash LIDAR system, such as illumination source, optics, and the architecture of the designated SPAD-based CMOS image sensor. Together with the modeling of the background noise and target topology, all of the fundamental factors that are involved in a typical LIDAR acquisition system have been included in order to predict the achievable system performance and verified with an existing sensor.

Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.