Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Bettelli, Manuele; Vurro, Filippo; Pecori, Riccardo; Janni, Michela; Coppedè, Nicola; Zappettini, A.; Tessera, Daniele

doi:10.1109/access.2023.3265597

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…Moreover, a high correlation between the bioristor R index and common vegetative indices used in field monitoring was observed, as well as a significant correlation with the crop stress water index [25]. Based on these results, bioristor was proposed as a tool for field phenotyping [25], coupled with a model based on artificial intelligence (AI) to forecast water stress in tomatoes [29]. Overall, bioristor proved to be able to monitor the functional physiology of apples, grapes, and kiwis [30].…”

Section: Introductionmentioning

confidence: 92%

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Kiwi 4.0: In Vivo Real-Time Monitoring to Improve Water Use Efficiency in Yellow Flesh Actinidia chinensis

Vurro,

Manfrini,

Boini

et al. 2024

Biosensors

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This manuscript reports the application of sensors for water use efficiency with a focus on the application of an in vivo OECT biosensor. In two distinct experimental trials, the in vivo sensor bioristor was applied in yellow kiwi plants to monitor, in real-time and continuously, the changes in the composition and concentration of the plant sap in an open field during plant growth and development. The bioristor response and physiological data, together with other fruit sensor monitoring data, were acquired and combined in both trials, giving a complete picture of the biosphere conditions. A high correlation was observed between the bioristor index (ΔIgs), the canopy cover expressed as the fraction of intercepted PAR (fi_PAR), and the soil water content (SWC). In addition, the bioristor was confirmed to be a good proxy for the occurrence of drought in kiwi plants; in fact, a period of drought stress was identified within the month of July. A novelty of the bioristor measurements was their ability to detect in advance the occurrence of defoliation, thereby reducing yield and quality losses. A plant-based irrigation protocol can be achieved and tailored based on real plant needs, increasing water use sustainability and preserving high-quality standards.

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Section: Introductionmentioning

confidence: 92%

“…The bioristor sensors was fabricated, installed, and operated following previously reported methods [23,25,29].…”

Section: Oect Sensor Device: Bioristor Preparation and Insertion In T...mentioning

confidence: 99%

Kiwi 4.0: In Vivo Real-Time Monitoring to Improve Water Use Efficiency in Yellow Flesh Actinidia chinensis

Vurro,

Manfrini,

Boini

et al. 2024

Biosensors

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“…In [132], an automated drip irrigation system is described, integrated with real-time water content sensors in the soil. Bettelli et al (2023) [129] have developed a model that characterizes, classifies, predicts the water stress of the crop, and from the above, irrigation is automated. A model of assistance is presented by Mohapatra et al ( 2019) [140] that from the prediction of soil moisture content over periods with a Neural Network (NN), irrigation is controlled, and SMS notifications are generated for farmers using Fuzzy Logic, in addition to generating statistics; the system can compensate for the amount of water lost through evapotranspiration.…”

Section: Irrigationmentioning

confidence: 99%

Agro-Technological Systems in Traditional Agriculture Assistance: A Systematic Review

Montalvo-Romero,

Montiel-Rosales,

Purroy-Vásquez

et al. 2023

IEEE Access

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Guaranteeing food security from agriculture in an uncertain context, derived from the effects of multiple factors, is a challenge. Traditional agricultural production is the one that faces the greatest challenges, derived from the scarce evolution in agricultural practices, despite being the one that contributes the most to the availability of food, at 80%. This systematic review aims to identify and analyze agrotechnological systems belonging to precision agriculture, which may be potentially adaptable to traditional rural agriculture. Contributions that improved crop yields from scientific and technological studies were analyzed. The PRISMA statement was used as a formal outline to collect and analyze 114 studies from the period 2018-2023. From the review, it was identified that there is a growing trend in the adoption of intelligent systems that help producers in the management of crops, accentuated in the increase of crop yield, in the determination of product quality, and in the management of water resources, mainly. Likewise, it was identified that the preponderant approach is the monitoring and control of crop development. This is achieved through emerging technologies, such as the Internet of Things, artificial intelligence, and machine learning, with information mainly collected by sensors embedded in drones, algorithms, decision support systems, sensors, and Arduino technology systems. Finally, this review shows that there are five viable systems that can be adapted to traditional agriculture to strengthen agricultural production. Therefore, the adoption of scientific-technological contributions from precision agriculture contributes to ensuring food security.INDEX TERMS Agricultural applications, food security, precision agriculture.

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Crop Yield Prediction Using Optimized Convolutional Neural Network Model Based on Environmental and Phenological Data

Haldorai,

Murugan

et al. 2024

EAI/Springer Innovations in Communication and Computing

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Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Abstract: The authors would like to thank Fondazione Cariparma (project Biomontans), RGVFAO VI DM 10271, and ALSIA Metaponto Agrobios for hosting some of the experiments and for funding a PhD doctoral fellowship.

Cited by 10 publications

References 37 publications

Kiwi 4.0: In Vivo Real-Time Monitoring to Improve Water Use Efficiency in Yellow Flesh Actinidia chinensis

Kiwi 4.0: In Vivo Real-Time Monitoring to Improve Water Use Efficiency in Yellow Flesh Actinidia chinensis

Agro-Technological Systems in Traditional Agriculture Assistance: A Systematic Review

Crop Yield Prediction Using Optimized Convolutional Neural Network Model Based on Environmental and Phenological Data

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