Search Results (10)

Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems. Full article

Harmful algal blooms (HABs) pose increasing threats to marine ecosystems and fisheries worldwide, creating an urgent need for efficient wide-area monitoring schemes. Satellite remote sensing offers a promising approach. However, quantitative, real-time HAB monitoring via satellites remains underdeveloped. Here, we evaluated the applicability of the Normalized Red Tide Index (NRTI), originally developed for Korean waters using the Geostationary Ocean Color Imager (GOCI), in detecting and quantifying HAB in the southern California Current. Our integrated monitoring encompassed two distinct regions of the California Current—Monterey Bay (central California) and La Bocana (Baja California)—separated by a 1470-km stretch of coastline and characterized by blooms of multiple HAB species. Our objectives were threefold: (1) to validate the relationship between NRTI and HAB cell densities through field measurements, (2) to evaluate the performance of hyperspectral NRTI derived from in situ reflectance measurements compared to existing multispectral indices including MODIS ocean color products, and (3) to assess the capability of multispectral sensors to represent NRTI by comparing multispectral-derived indices against hyperspectral NRTI measurements. We found species-specific relationships between hyperspectral NRTI and in situ HAB cell densities, with Prorocentrum gracile in Baja California showing a robust logarithmic fit (R² = 0.92) and multi-species assemblage (dominated by Akashiwo sanguinea) in Monterey Bay displaying a weak, positive correlation. MODIS-derived NRTI values were consistently lower than hyperspectral estimates due to reduced spectral resolution, but the two datasets were strongly correlated (R² = 0.97), allowing for reliable tracking of relative bloom intensity. MODIS applications further captured distinct bloom dynamics across regions, with localized nearshore blooms in Baja California and broader offshore expansion in Monterey Bay. These results suggest that the NRTI-based monitoring scheme can effectively quantify HAB intensity across broad geographic scales, but its application requires explicit consideration of regional HAB assemblages. Full article

Grenache wines from the Central Coast of California were subjected to different alcoholic fermentation temperature regimes (Cold, Cold/Hot, Hot) and cap management protocols, namely, punch down (PD), or no punch down (No PD), to determine the effect of these practices on the color, aroma, and the retronasal and mouthfeel sensory characteristics of the resulting wines. Descriptive analysis (n = 8, line scale rating 0–15) results indicated that the combination of a hot fermentation temperature and no punch downs led to a significantly higher intensity in perceived color saturation (7.89) and purple hue (8.62). A two-way analysis of variance (ANOVA) showed that cap management was significantly more impactful on the perception of orthonasal aromas than fermentation temperature. The reduction aroma was significantly higher in No PD wines (5.02) compared to PD wines (3.50), while rose and hot aromas had significantly higher intensity perception for PD wines (5.18, 6.80) than for No PD wines (6.80, 6.14). Conversely, analysis of selected volatile compounds indicated that fermentation temperature was more impactful than cap management regime. Cold/Hot wines had higher concentrations of important esters such as ethyl hexanoate (650 µg/L) and isoamyl acetate (992 µg/L). Cold wines had a higher concentration of β-damascenone (0.719 µg/L). TCATA evaluation (n = 8) indicated that Cold/Hot PD wines had a significantly higher citation proportion of fruit flavor (1.0) and velvet astringency perception (0.80) without significant reduction flavors. Finally, the present study represents a contribution with the main volatile compounds (e.g., β-damascenone and esters in the Cold and Cold/Hot fermented wines, respectively; hexanol in PD wines, which may be potentially responsible for a hot mouthfeel), and sensory characteristics (red fruit, tropical fruit, white pepper, and rose) of Grenache wines grown in the Mediterranean climate of the Central Coast of California. Full article

Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-N_UP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVI_UAS and NDRE_UAS (average ground sampling distance: 3.7 cm pixel⁻¹), and a proximal GreenSeeker (GS) sensor was used to record NDVI_GS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVI_UAS had a narrower range of values that were poorly differentiated compared to NDVI_GS and NDRE_UAS. The critical PI-N_UP where yields did not increase with higher PI-N_UP averaged 109 kg N ha⁻¹ (±4 kg N ha⁻¹). The relationship between SI and PI-N_UP for the NDVI_UAS saturated lower than this critical PI-N_UP (96 kg N ha⁻¹), whereas NDVI_GS and NDRE_UAS saturated at 111 and 130 kg N ha⁻¹, respectively. This indicates that NDVI_UAS was less suitable for making N management decisions at this crop stage than NDVI_GS and NDRE_UAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVI_UAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha⁻¹ per 0.1 SI). In contrast, the slopes for NDVI_GS and NDRE_UAS were 0.9 and 1.1 Mg ha⁻¹ per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVI_GS and NDRE_UAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVI_UAS was more limited. Full article

The results reported in this work are based in part on measurements of sap flow in a few select trees on a representative riparian forest plot coupled with a forest-wide randomized sampling of tree sapwood area in a watershed located along the Pacific coast in Santa Cruz County, California. These measurements were upscaled to estimate evapotranspiration (ET) across the forest and to quantify groundwater usage by dominant phreatophyte vegetation. Canopy cover in the study area is dominated by red alder (Alnus rubra) and arroyo willow (Salix lasiolepis), deciduous phreatophyte trees from which a small sample was selected for instrumentation with sap flow sensors on a single forest plot. These localized sap flow measurements were then upscaled to the entire riparian forest to estimate forest ET using data from a survey of sapwood area on six plots scattered randomly across the entire forest. The estimated canopy-scale ET was compared to reference ET and NDVI based estimates. The results show positive correlation between sap flow based estimates and those of the other two methods, though over the winter months, sap flow-based ET values were found to significantly underestimate ET as predicted by the other two methods. The results illustrate the importance of ground-based measurements of sap flow for calibrating satellite based methods and for providing site-specific estimates and to better characterize the ET forcing in groundwater flow models. Full article

Aonidiella aurantii is one of the most damaging armored scales in citrus crops worldwide. To control this pest, high water volume rates are conventionally used. In order to rationalize the pesticide applications in citrus, IVIA developed CitrusVol, a tool that recommends the optimal volume rate based on the vegetation, the pest or disease and the active ingredient. In this study the objectives were: (i) validate CitrusVol as a tool to adjust the spray volume to control A. aurantii and (ii) quantify its environmental and economical advantages. For this, the spray volume adjusted with CitrusVol was compared with the one conventionally used by farmers in 18 applications in seven orchards during two years. The following parameters were evaluated: (i) spray distribution in the canopy, (ii) A. aurantii males trapped per day, and (iii) number of scales per fruit at harvest. CitrusVol reduced the spray volume and the amount of pesticide by 35% on average. Despite this reduction, a satisfactory spray distribution was achieved, and the volume was found to control the pest in a comparable way to the conventional volume. Moreover, CitrusVol saved per application and on average 31.25 h/100 ha of spray operating time, 241.83 L/100 ha of fuel consumption and consequently, the reduction of emissions of CO₂ was 631.18 kg/100 ha. Therefore, CitrusVol allows for efficient, low-input and low-impact pesticide applications. Full article

Recently, both single modality and cross modality near-duplicate image detection tasks have received wide attention in the community of pattern recognition and computer vision. Existing deep neural networks-based methods have achieved remarkable performance in this task. However, most of the methods mainly focus on the learning of each image from the image pair, thus leading to less use of the information between the near duplicate image pairs to some extent. In this paper, to make more use of the correlations between image pairs, we propose a spatial transformer comparing convolutional neural network (CNN) model to compare near-duplicate image pairs. Specifically, we firstly propose a comparing CNN framework, which is equipped with a cross-stream to fully learn the correlation information between image pairs, while considering the features of each image. Furthermore, to deal with the local deformations led by cropping, translation, scaling, and non-rigid transformations, we additionally introduce a spatial transformer comparing CNN model by incorporating a spatial transformer module to the comparing CNN architecture. To demonstrate the effectiveness of the proposed method on both the single-modality and cross-modality (Optical-InfraRed) near-duplicate image pair detection tasks, we conduct extensive experiments on three popular benchmark datasets, namely CaliforniaND (ND means near duplicate), Mir-Flickr Near Duplicate, and TNO Multi-band Image Data Collection. The experimental results show that the proposed method can achieve superior performance compared with many state-of-the-art methods on both tasks. Full article

Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO₂ uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIR_v) index. EVI and NIR_v were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO₂ uptake (A_leaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in A_leaf. EVI and NIR_v showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with A_leaf, and EVI and NIR_v consequently mimic GPP’s multiplicative sensitivity to LAI and A_leaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIR_v’s sensitivity to both LAI and A_leaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP. Full article

The volatile organic compounds (VOCs) released from herbivore-infested plants can be used as chemical signals by parasitoids during host location. In this research, we investigated the VOC chemical signals for the parasitoid Aphytis melinus to discriminate between Aonidiella aurantii (California red scale)-infested fruit and non-infested fruit on three different citrus species. First, we identified the chemical stimuli emanating from non-infested and A. aurantii-infested citrus fruits via solid phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS) analyses and identified 34 volatile organic compounds (VOCs). The GC-MS analysis showed qualitative and quantitative differences between VOCs emitted from non-infested and infested citrus fruit. Two VOCs, d-limonene and β-ocimene, were significantly increased in all infested fruit, regardless of the fruit species. The response of the female adult A. melinus to olfactory cues associated with A. aurantii infested fruit was evaluated using a Y-tube olfactometer. In two-choice behavioural assays, A. melinus females preferred infested citrus cues over non-infested fruit. Females showed positive chemotaxis toward these VOCs in all tested combinations involving two dosages of synthetic compounds, d-limonene and β-ocimene, except for d-limonene at a dosage of 10 μL/mL. The application of these VOCs may help to enhance the effectiveness of bio-control programs and parasitoid mass-rearing techniques. Full article

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales. Full article