An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation

Abstract

Precision irrigation represents those strategies aiming to feed the plant needs following the soil’s spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.

A bibliometric and thematic approach to agriculture 4.0

“…So far, we find two research fronts: 1) researchers concerned with the impacts of agriculture 4.0 ( Klerkx and Rose, 2020 ; Rose and Chilvers, 2018 ), and 2) researchers focussed on the technological development of agriculture 4.0 ( Figorilli et al., 2021 ; Li et al., 2008 ; Wang et al., 2016 ).…”

“…The cluster composed by authors Costa, C. Antonucci, F. Figorilli, S. and Pallottino, F. has thick lines. These authors explore issues related to infrared vision for weed detection, devices for optimising water use in irrigation, and stereo computer vision systems for agricultural machinery ( Costa et al., 2019 ; Figorilli et al., 2021 ; Pallottino et al., 2020 ). Thus, the authors of the two largest co-authorship networks work on solutions for digital agriculture development.…”

Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors

“…The prices of the most reliable soil water content sensors range between USD 150 and USD 5000, thus positioning these sensors far from the IoT world. Instead, the reliability of very low-cost soil water content sensors easily purchasable in the worldwide internet market is still a matter of scientific debate [ 8 , 32 , 33 , 34 , 35 , 36 , 37 ] as further highlighted in the next sections.…”

Prototype for automating the irrigation system using the arduino UNO R3 prototyping board for water control

“…Segundo Routis e Roussaki (2023), um dos setores que mais utiliza água é a agricultura irrigada, tanto para a manutenção como para o aumento da produtividade, com um consumo global de aproximadamente 70% dos recursos hídricos disponíveis, o que gera grande preocupação e torna-se uma das principais dificuldades para o desenvolvimento sustentável. Figorilli et al (2021) salientam que a agricultura tradicional utiliza os recursos hídricos de forma demasiada, provocando desperdício tanto de água como de energia elétrica, uma vez que não considera as condições ambientais do solo e nem das plantas. Nesse sentido, a busca por novas tecnologias para mitigar a escassez dos recursos hídricos são necessárias para utilizar quantidades de água de forma adequada e sustentável.…”