HortNet417v1—A Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress

The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.

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The Decrease of Leaf Dark Respiration during Water Stress Is Related to Leaf Non-Structural Carbohydrate Pool in Vitis vinifera L.

Plants ◽

10.3390/plants11010036 ◽

2021 ◽

Vol 11 (1) ◽

pp. 36

Author(s):

Sergio Tombesi ◽

Tommaso Frioni ◽

Francesca Grisafi ◽

Paolo Sabbatini ◽

Stefano Poni ◽

...

Keyword(s):

Water Stress ◽

Dark Respiration ◽

Soluble Sugars ◽

Water Shortage ◽

Stress Conditions ◽

Plant Water ◽

Plant Water Stress ◽

Leaf Dark Respiration ◽

Structural Carbohydrate ◽

The Impact

Dark respiration (Rd) is a fundamental plant process used to gain biomass and maintain plant physiological activity. It accounts for the metabolization of a large share of the carbon fixed by photosynthesis. However, Rd during conditions of severe plant water stress is still poorly understood. The decrease in leaf transpiration increases temperature, one of the most important drivers of leaf Rd. On the other hand, water stress decreases the pool of leaf carbohydrates, which are the most important substrate for respiration. The aim of the present work was to determine the impact of water shortage on leaf Rd in grapevine and understand the driving factors in modulating leaf Rd response under plant water stress conditions. Water stressed vines had lower Rd as the water shortage severity increased. Rd was correlated with leaf temperature in well-watered vines. Instead, in water stressed vines, Rd correlated with leaf soluble sugars. The decrease of leaf Rd in water stressed vines was due to the decrease of leaf non-structural carbohydrate that, under water stress conditions, exerted a limiting effect on Rd.

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Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Applied Sciences ◽

10.3390/app11041403 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1403

Author(s):

Mohd Hider Kamarudin ◽

Zool Hilmi Ismail ◽

Noor Baity Saidi

Keyword(s):

Deep Learning ◽

Water Stress ◽

Sensor Fusion ◽

Water Status ◽

System Modeling ◽

Economic Loss ◽

Plant Water ◽

Plant Water Stress ◽

Stress Assessment ◽

Intelligence System

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content

New Phytologist ◽

10.1111/nph.17254 ◽

2021 ◽

Author(s):

Xiangtao Xu ◽

Alexandra G. Konings ◽

Marcos Longo ◽

Andrew Feldman ◽

Liang Xu ◽

...

Keyword(s):

Water Stress ◽

Surface Water ◽

Tropical Forest ◽

Diurnal Variation ◽

Forest Canopy ◽

Leaf Surface ◽

Plant Water ◽

Plant Water Stress ◽

Canopy Water ◽

Tropical Forest Canopy

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Leaf compatible “eco-friendly” temperature sensor clip for high density monitoring wireless networks

Wireless Power Transfer ◽

10.1017/wpt.2017.1 ◽

2017 ◽

Vol 4 (1) ◽

pp. 55-60 ◽

Cited By ~ 5

Author(s):

Valeria Palazzari ◽

Paolo Mezzanotte ◽

Federico Alimenti ◽

Francesco Fratini ◽

Giulia Orecchini ◽

...

Keyword(s):

Decision Support ◽

Water Stress ◽

Temperature Sensor ◽

Irrigation System ◽

Fuel Oil ◽

Plant Water ◽

Temperature Differential ◽

Water Pump ◽

Plant Water Stress ◽

System A

This paper describes the design, realization, and application of a custom temperature sensor devoted to the monitoring of the temperature differential between the leaf and the air. This difference is strictly related to the plant water stress and can be used as an input information for an intelligent and flexible irrigation system. A wireless temperature sensor network can be thought as a decision support system used to start irrigation when effectively needed by the cultivation, thus saving water, pump fuel oil, and preventing plant illness caused by over-watering.

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Reducing the Excessive Evaporative Demand Improved the Water-use Efficiency of Greenhouse Cucumber by Regulating the Trade-off between Irrigation Demand and Plant Productivity

HortScience ◽

10.21273/hortsci13129-18 ◽

2018 ◽

Vol 53 (12) ◽

pp. 1784-1790 ◽

Cited By ~ 4

Author(s):

Dalong Zhang ◽

Yuping Liu ◽

Yang Li ◽

Lijie Qin ◽

Jun Li ◽

...

Keyword(s):

Water Stress ◽

Water Use Efficiency ◽

Water Flow ◽

Water Use ◽

Plant Productivity ◽

Plant Water ◽

Evaporative Demand ◽

Plant Water Stress ◽

Irrigation Demand ◽

Use Efficiency

Although atmospheric evaporative demand mediates water flow and constrains water-use efficiency (WUE) to a large extent, the potential to reduce irrigation demand and improve water productivity by regulating the atmospheric water driving force is highly uncertain. To bridge this gap, water transport in combination with plant productivity was examined in cucumber (Cucumis sativus L.) grown at contrasting evaporative demand gradients. Reducing the excessive vapor pressure deficit (VPD) decreased the water flow rate, which reduced irrigation consumption significantly by 16.4%. Reducing excessive evaporative demand moderated plant water stress, as leaf dehydration, hydraulic limitation, and excessive negative water potential were prevented by maintaining water balance in the low-VPD treatment. The moderation of plant water stress by reducing evaporative demand sustained stomatal function for photosynthesis and plant growth, which increased substantially fruit yield and shoot biomass by 20.1% and 18.4%, respectively. From a physiological perspective, a reduction in irrigation demand and an improvement in plant productivity were achieved concomitantly by reducing the excessive VPD. Consequently, WUE based on the criteria of plant biomass and fruit yield was increased significantly by 43.1% and 40.5%, respectively.

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