Soft computing applied to stem water potential estimation: A fuzzy rule based approach

Valdés-Vela, Mercedes; Abrisqueta, Isabel; Conejero, Wenceslao; Vera, Juan; Ruiz-Sánchez, M.C.

doi:10.1016/j.compag.2015.05.019

Cited by 26 publications

(14 citation statements)

References 33 publications

(38 reference statements)

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“…These authors reported the feasibility of using Ψ stem as the plant water status indicator, not only for its robustness but also for its stability in successive growing seasons. Moreover, a model based on soil water content and meteorological variables that provides information on plant water status has been proposed as a guide for irrigation scheduling of early-maturing peach trees under Mediterranean conditions [67].…”

Section: Remote and Terrestrial Plant-water-status Indicatorsmentioning

confidence: 99%

Terrestrial and Remote Indexes to Assess Moderate Deficit Irrigation in Early-Maturing Nectarine Trees

et al. 2019

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Monitoring plant water status is relevant for the sustainable management ofirrigation under water deficit conditions. Two treatments were applied to an early-maturingnectarine orchard: control (well irrigated) and precise deficit irrigation (PDI, based on soilwater content thresholds). Moderate water deficits generated by PDI were assessed bycomparing terrestrial: stem water potential (Ψstem) and gas exchange parameters, with remote:canopy temperature, normalized difference vegetation (NDVI), and soil adjusted vegetationindex (SAVI), plant water status indicators. The Ψstem was the only indicator that showedsignificant differences between treatments. NDVI and SAVI at the postharvest period wereappropriate indexes for estimating winter pruning, although they did not serve well as plantstress indicator. Vapor pressure deficit along with Ψstem values were able to predict remotesensing data. Ψstem and canopy to air temperature difference values registered the highestsignal intensity and NDVI the highest sensitivity for detecting water deficit situations. Theresults suggest that care should be taken when using instantaneous remote indicators toevaluate moderate water deficits in deciduous fruit trees; more severe/longer water stressconditions are probably needed. The proposed PDI strategy promoted water saving whilemaintaining yield, and could be considered a promising tool for semi-arid agrosystems.

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Section: Remote and Terrestrial Plant-water-status Indicatorsmentioning

confidence: 99%

Terrestrial and Remote Indexes to Assess Moderate Deficit Irrigation in Early-Maturing Nectarine Trees

et al. 2019

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“…Much research has been specifically conducted to bridge this gap [32][33][34][35][36]. Nevertheless, there are very few analyses focused on identifying the farm conditions that make the techniques developed in these studies profitable.…”

Section: Introductionmentioning

confidence: 99%

Trends and Challenges in Irrigation Scheduling in the Semi-Arid Area of Spain

García

Lecina²,

Ruiz-Sánchez

et al. 2020

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A growing international human population and rising living standards are increasing the demand for agricultural products. Under higher pressure over natural resources, environmental concerns are increasing as well, challenging current water use decision-making processes in irrigated agriculture. Higher agricultural productivity means water should be applied more efficiently, which requires instant information on weather, soil, and plant conditions throughout the growing season. An information-based irrigation scheduling application tightened to the spatiotemporal variability of the fields is critical for enhancing the current irrigation system and making better irrigation scheduling decisions. The aim of this study is to review current irrigation scheduling methodologies based on two case studies (woody and field crops) located in semi-arid areas of Southeast Spain. We realize that optimal irrigation programming requires consistent investment in equipment, expenditure on operation and maintenance, and qualified technical and maintenance services. These technological approaches will be worthwhile in farms with low water availability, high profitability, and significant technical-economic capacity.

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“…The information was used to control the position of a bucket that was the receiver of a signal. Meanwhile, [11] developed an innovative fuzzy system to accurately grade leaf diseases, and [12] implemented a fuzzy inference system to estimate stem water potential. According to Dengel in [13], agriculture offers a vast application area for a wide variety of AI core technologies.…”

Section: Introductionmentioning

confidence: 99%

Towards Smart Agriculture Monitoring Using Fuzzy Systems

et al. 2021

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Conventional farming is labor-consuming and the need to continuously monitor crops can be a burden for farmers. By realizing the concept of smart farming based on Internet of Things (IoT) technology, farmers can use a mobile application to observe and monitor air humidity, air temperature, and soil moisture-factors that can affect plant growth. Furthermore, the use of timers to control the pumps in conventional watering systems is not always practical in real-life cases. This paper proposes a framework that enables advanced fuzzy logic to control a pump's switching time according to user-defined variables, whereby sensors are the main aspect of and contributor to the system. Our proposed idea offers great potential for excellent performance as an interface between the sensors as the input and the IoT as the output medium. A comparison is made between the proposed system and manual handling. The results prove that the water consumption and watering time has been reduced significantly.

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Soft computing applied to stem water potential estimation: A fuzzy rule based approach

Cited by 26 publications

References 33 publications

Terrestrial and Remote Indexes to Assess Moderate Deficit Irrigation in Early-Maturing Nectarine Trees

Terrestrial and Remote Indexes to Assess Moderate Deficit Irrigation in Early-Maturing Nectarine Trees

Trends and Challenges in Irrigation Scheduling in the Semi-Arid Area of Spain

Towards Smart Agriculture Monitoring Using Fuzzy Systems

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