End-to-end deep learning for directly estimating grape yield from ground-based imagery

Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Continuous data collection using a vehicle-mounted sensing kit combined with collection of ground truth yield data at harvest using a commercial yield monitor allowed for the generation of a large dataset of 23,581 yield points and 107,933 images. Moreover, this study was conducted in a mechanically managed commercial vineyard, representing a challenging environment for image analysis but a common set of conditions in the California Central Valley. Three model architectures were tested: object detection, CNN regression, and transformer models. The object detection model was trained on hand-labeled images to localize grape bunches, and either bunch count or pixel area was summed to correlate with grape yield. Conversely, regression models were trained end-to-end to predict grape yield from image data without the need for hand labeling. Results demonstrated that both a transformer as well as the object detection model with pixel area processing performed comparably, with a mean absolute percent error of 18% and 18.5%, respectively on a representative holdout dataset. Saliency mapping was used to demonstrate the attention of the CNN model was localized near the predicted location of grape bunches, as well as on the top of the grapevine canopy. Overall, the study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale. Additionally, the end-to-end modeling approach was able to perform comparably to the object detection approach while eliminating the need for hand-labeling

Novel algorithms for high-resolution prediction of canopy evapotranspiration in grapevine

Developing low-cost technology for custom water delivery to individual or small groups of plants is a critical next step to advance precision irrigation. Current systems for estimating evapotranspiration (ET), or plant water use, work on the scale of a full vineyard (e.g., 3–5 acres) or the scale of a single vine, but at a cost that prohibits monitoring past a small number of representative vines. To develop and evaluate low-cost ET sensors for individual grapevines, we used three head-pruned Zinfandel vines in pots and placed them on load cells to collect continuous weights indicative of actual ET. We mounted research-grade sensors for humidity, temperature, and wind speed on each vine and saved data at 2-minute intervals during three growing seasons. We developed three models based on first principles (Convective Mass Transfer or Mass Balance approaches) or simple correlations to predict actual single-plant ET from these data. We present here the results of a multi-year trial at the UC-Davis RMI vineyard to illustrate the performance of each of the models for ET estimation. Relative model performance was assessed by comparing model predictions to ground truth data provided by measurements from load cells–including assessments of estimated instantaneous ET rate, estimated cumulative water use over a one-hour window surrounding solar noon, and estimated cumulative water use over a full 24-hour period. The three algorithms developed consistently performed well, with single vine ET rate predictions showing a strong linear relationship with ground truth (range in r2 over three seasons CMT r2 = 0.61–0.86; MB r2 = 0.07–0.91; EM r2 = 0.57–0.92). The MB approach, which includes two measurements of relative humidity and temperature, was the most variable, likely due to the impact of sensor placement. In all seasons, we also examined the trend in the plant scaling factor found in each model, deemed As, which, based on model theory, is a function of vine size. Taken together, these results suggest that high-resolution irrigation (HRI) models are a promising new method for ET estimation at the single plant level

Frost Induces Respiration and Accelerates Carbon Depletion in Trees.

Cellular respiration depletes stored carbohydrates during extended periods of limited photosynthesis, e.g. winter dormancy or drought. As respiration rate is largely a function of temperature, the thermal conditions during such periods may affect non-structural carbohydrate (NSC) availability and, ultimately, recovery. Here, we surveyed stem responses to temperature changes in 15 woody species. For two species with divergent respirational response to frost, P. integerrima and P. trichocarpa, we also examined corresponding changes in NSC levels. Finally, we simulated respiration-induced NSC depletion using historical temperature data for the western US. We report a novel finding that tree stems significantly increase respiration in response to near freezing temperatures. We observed this excess respiration in 13 of 15 species, deviating 10% to 170% over values predicted by the Arrhenius equation. Excess respiration persisted at temperatures above 0 °C during warming and reoccurred over multiple frost-warming cycles. A large adjustment of NSCs accompanied excess respiration in P. integerrima, whereas P. trichocarpa neither excessively respired nor adjusted NSCs. Over the course of the years included in our model, frost-induced respiration accelerated stem NSC consumption by 8.4 mg (glucose eq.) cm(-3) yr(-1) on average in the western US, a level of depletion that may continue to significantly affect spring NSC availability. This novel finding revises the current paradigm of low temperature respiration kinetics

Excess Diffuse Light Absorption in Upper Mesophyll Limits CO2 Drawdown and Depresses Photosynthesis.

In agricultural and natural systems, diffuse light can enhance plant primary productivity due to deeper penetration into and greater irradiance of the entire canopy. However, for individual sun-grown leaves from three species, photosynthesis is actually less efficient under diffuse compared with direct light. Despite its potential impact on canopy-level productivity, the mechanism for this leaf-level diffuse light photosynthetic depression effect is unknown. Here, we investigate if the spatial distribution of light absorption relative to electron transport capacity in sun- and shade-grown sunflower (Helianthus annuus) leaves underlies its previously observed diffuse light photosynthetic depression. Using a new one-dimensional porous medium finite element gas-exchange model parameterized with light absorption profiles, we found that weaker penetration of diffuse versus direct light into the mesophyll of sun-grown sunflower leaves led to a more heterogenous saturation of electron transport capacity and lowered its CO2 concentration drawdown capacity in the intercellular airspace and chloroplast stroma. This decoupling of light availability from photosynthetic capacity under diffuse light is sufficient to generate an 11% decline in photosynthesis in sun-grown but not shade-grown leaves, primarily because thin shade-grown leaves similarly distribute diffuse and direct light throughout the mesophyll. Finally, we illustrate how diffuse light photosynthetic depression could overcome enhancement in canopies with low light extinction coefficients and/or leaf area, pointing toward a novel direction for future research

Excess Diffuse Light Absorption in Upper Mesophyll Limits CO2 Drawdown and Depresses Photosynthesis.

In agricultural and natural systems, diffuse light can enhance plant primary productivity due to deeper penetration into and greater irradiance of the entire canopy. However, for individual sun-grown leaves from three species, photosynthesis is actually less efficient under diffuse compared with direct light. Despite its potential impact on canopy-level productivity, the mechanism for this leaf-level diffuse light photosynthetic depression effect is unknown. Here, we investigate if the spatial distribution of light absorption relative to electron transport capacity in sun- and shade-grown sunflower (Helianthus annuus) leaves underlies its previously observed diffuse light photosynthetic depression. Using a new one-dimensional porous medium finite element gas-exchange model parameterized with light absorption profiles, we found that weaker penetration of diffuse versus direct light into the mesophyll of sun-grown sunflower leaves led to a more heterogenous saturation of electron transport capacity and lowered its CO2 concentration drawdown capacity in the intercellular airspace and chloroplast stroma. This decoupling of light availability from photosynthetic capacity under diffuse light is sufficient to generate an 11% decline in photosynthesis in sun-grown but not shade-grown leaves, primarily because thin shade-grown leaves similarly distribute diffuse and direct light throughout the mesophyll. Finally, we illustrate how diffuse light photosynthetic depression could overcome enhancement in canopies with low light extinction coefficients and/or leaf area, pointing toward a novel direction for future research