Robotics-based vineyard water potential monitoring at high resolution

Sáiz-Rubio, Verónica; Rovira-Más, Francisco; Cuenca-Cuenca, Andrés; Alves, Fernando

doi:10.1016/j.compag.2021.106311

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…Three sets of Spectral Reflectance Sensors (METER Group, Pullman, WA, USA) were installed in each of the three blocks (B2, B4, and B5), specifically targeting three WI plots, three MI plots, and two DI plots ( Figure 1 ). SRS has been used in many studies for phenotyping and stress detection over agricultural fields ( Bai et al., 2016 ; Magney et al., 2016 ; Pérez-ruiz et al., 2020 ; Saiz-Rubio et al., 2021 ) and measuring the light use efficiency and tracking the pigment change of forests ( Gamon et al., 2015 ; Castro and Sanchez-Azofeifa, 2018 ; Eitel et al., 2019 ). We installed the SRS sensors 1.1 m above the tomato canopy on fixed stands after transplanting.…”

Section: Methodsmentioning

confidence: 99%

Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery

et al. 2023

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Tracking plant water status is a critical step towards the adaptive precision irrigation management of processing tomatoes, one of the most important specialty crops in California. The photochemical reflectance index (PRI) from proximal sensors and the high-resolution unmanned aerial vehicle (UAV) imagery provide an opportunity to monitor the crop water status efficiently. Based on data from an experimental tomato field with intensive aerial and plant-based measurements, we developed random forest machine learning regression models to estimate tomato stem water potential (ψstem), (using observations from proximal sensors and 12-band UAV imagery, respectively, along with weather data. The proximal sensor-based model estimation agreed well with the plant ψstem with R2 of 0.74 and mean absolute error (MAE) of 0.63 bars. The model included PRI, normalized difference vegetation index, vapor pressure deficit, and air temperature and tracked well with the seasonal dynamics of ψstem across different plots. A separate model, built with multiple vegetation indices (VIs) from UAV imagery and weather variables, had an R2 of 0.81 and MAE of 0.67 bars. The plant-level ψstem maps generated from UAV imagery closely represented the water status differences of plots under different irrigation treatments and also tracked well the temporal change among flights. PRI was found to be the most important VI in both the proximal sensor- and the UAV-based models, providing critical information on tomato plant water status. This study demonstrated that machine learning models can accurately estimate the water status by integrating PRI, other VIs, and weather data, and thus facilitate data-driven irrigation management for processing tomatoes.

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Section: Methodsmentioning

confidence: 99%

Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery

et al. 2023

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“…Here, pictures were taken from on-branches coffee fruits with a diverse higher rates by autonomous systems has been proposed in different studies. For example, an autonomous robot to monitor vineyard water potential was proposed by Saiz-Rubio et al (2021). Autonomous robots have even been proposed to perform actions, such as tomato harvesting (Liu et al, 2020), strawberry harvesting (Xiong et al, 2020), and weed control (Wu et al, 2020b).…”

Section: Performance By Detection Classmentioning

confidence: 99%

Detection of coffee fruits on tree branches using computer vision

Bazame

Molin

Althoff

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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“…The ground platforms used for proximal sensing can be grouped into three categories: portable, self-supporting in the field, and mounted on tractors or agricultural machinery or UGVs [119]. Regarding the use of UGVs in precision oliviculture, there have been no scientific applications in the literature yet, although the progress made in other sectors bodes well for their future application in olive cultivation [120].…”

Section: Proximal Sensingmentioning

confidence: 99%

“…The application of remote sensing technologies for precision olive growing has increased rapidly in the last decades. The unprecedented availability of high-resolution (spatial, spectral, and temporal) satellite imagery has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient management, disease management, pest management, and yield forecasting [120].…”

Section: Future Directions Of Precision Oliviculturementioning

confidence: 99%

Precision Oliviculture: Research Topics, Challenges, and Opportunities—A Review

Roma

Catania

2022

Remote Sensing

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Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML).

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Robotics-based vineyard water potential monitoring at high resolution

Cited by 16 publications

References 36 publications

Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery

Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery

Detection of coffee fruits on tree branches using computer vision

Precision Oliviculture: Research Topics, Challenges, and Opportunities—A Review

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