Design of decision support system to identify crop water need

Poonia, Ramesh Chandra; Bhatnagar, Vaibhav

doi:10.1007/s12652-021-03291-w

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The authors of [138] optimize irrigation by precisely activating the water valve, employing neural networkbased prediction -and adjusted with Fuzzy Logic-of the soil water requirement one hour in advance; structural similarity (SSIM)-based to locate water-deficient regions. An agricultural decision support system is designed by Poonia and Bhatnagar (2023) [141] to indicate the crop´s water needs and is sent to a smartphone. In [132], an automated drip irrigation system is described, integrated with real-time water content sensors in the soil.…”

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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