A data-driven bibliometric review on precision irrigation

Violino, Simona; Figorilli, Simone; Ferrigno, Marianna; Manganiello, Veronica; Pallottino, Federico; Costa, Corrado; Menesatti, Paolo

doi:10.1016/j.atech.2023.100320

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…( 2) High-frequency phrases that do not include smart agriculture in smart irrigation are big data, food security, supply chain, agricultural production, smart city, cloud computing, and energy consumption. These research hotspots may be the next research direction of smart irrigation [10,26].…”

Section: Compare Smart Agriculture and Smart Irrigationmentioning

confidence: 99%

“…In addition, wireless sensor technology can be integrated with automation systems to automate agricultural production, including automatic irrigation, fertilization, agricultural machinery operations, etc., improving labor efficiency and reducing labor costs. Wireless sensor technology brings opportunities for informatization, automation and sustainable development to smart agriculture and is expected to promote more innovation and progress in the agricultural field [26].…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

“…Many papers have analyzed trends in SI. Simona conducted a bibliometric on SI and divided it into 10 clusters: internet, uncrewed aerial vehicles, crop coefficient, precision irrigation, lawn turf, irrigation system, cost-effective, machine vision, wireless sensors network, sprinkler irrigation, and energy saving, and using the most prominent techniques in cluster analysis are the IoT and machine learning [26]. Ahmed made a detailed analysis of SI management under climate change in drylands.…”

Section: Introductionmentioning

confidence: 99%

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Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Pang,

Marinello,

Tang

et al. 2023

Sustainability

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Agriculture is considered one of the most critical sectors that play a strategic role in ensuring food security. It is directly related to human development and social stability. The agricultural sector is currently incorporating new technologies from other areas. These phenomena are smart agriculture and smart irrigation. However, a challenge to research is the integration of technologies from different knowledge fields, which has caused theoretical and practical difficulties. Thus, our purpose in this study has been to understand the core of these two themes. We extracted publications in Scopus and used bibliometric methods for high-frequency word and phrase analysis. Research shows that current research on smart agriculture mainly focuses on the Internet of Things, climate change, machine learning, precision agriculture and wireless sensor networks. Simultaneously, the Internet of Things, irrigation systems, soil moisture, wireless sensor networks and climate change have received the most scholarly attention in smart irrigation. This study used cluster analysis to find that the IoT has the most apparent growth rate in smart agriculture and smart irrigation, with five-year growth rates of 1617% and 2285%, respectively. In addition, machine learning, deep learning and neural networks have enormous potential in smart irrigation compared with smart agriculture.

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Section: Compare Smart Agriculture and Smart Irrigationmentioning

confidence: 99%

Section: Wireless Sensor Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Pang,

Marinello,

Tang

et al. 2023

Sustainability

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“…In precision agriculture, imaging technologies have been gradually extended from fertilization to the analysis of the sources of intra-field variation, including plant diseases [17][18][19][20]. A key turning point in disease sensing concerns the accurate estimation of both the occurrence and severity, coupled with the monitoring of their spreading [21].…”

Section: Introductionmentioning

confidence: 99%

Autonecrotic Tomato (Solanum lycopersicum L.) Line as a Potential Model for Applications in Proximal Sensing of Biotic and Abiotic Stress

Santangelo,

Giudice,

Figorilli

et al. 2024

Agriculture

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The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between a tomato and Cladosporium fulvum, the fungal agent of leaf mold. The contemporary presence of an in-house allele encoding the Rcr3lyc protein and the resistance gene Cf-2pim (from Solanum pimpinellifolium) causes auto-necrosis on the leaves even in the absence of the pathogen (hybrid necrosis). The aim of the work was (i) to examine the potential value of the necrotic genotype as a model system for setting up theoretical guidance for monitoring the phytosanitary status of tomato plants and (ii) to develop a predictive model for the early detection of pathogens (or other stresses) in the tomato or other species. Eighteen IGSV tomato individuals at the 4–5th true-leaf stage were grown in three rows (six plants per row) considered to be replicates. The healthy control was the F1 hybrid Elisir (Olter). A second mutant line (SA410) deriving from a cross between the necrotic mutant and a mutant line of the lutescent (l) gene was used during foliar analysis via microspectrometry. The leaves of the mutants and normal plants were monitored through a portable VIS/NIR spectrometer SCIOTM (Consumer Physics, Tel Aviv, Israel) covering a spectral range between 740 and 1070 nm. Two months after the transplant, the acropetal progression of the autonecrosis showed three symptomatic areas (basal, median, apical) on each IGSV plant: necrotic, partially damaged, and green, respectively. Significantly lower chlorophyll content was found in the basal and median areas of IGSV compared with the control (Elisir). A supervised classification/modelling method (SIMCA) was used. Applying the SIMCA model to the dataset of 162 tomato samples led to the identification of the boundary between the healthy and damaged samples (translational critical distance). Two 10 nm wavelength ranges centred at 865 nm and 1055 nm exhibited a stronger link between symptomatology and spectral reflectance. Studies on specific highly informative mutants of the type described may allow for the development of predictive models for the early detection of pathogens (or other stresses) via proximal sensing.

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“…This is becoming a necessity; otherwise, insufficient water and food will cause disasters and chaos in the world. In order to prevent this situation, different technologies and many papers on this subject have been put forward [9]. While technological developments support many areas in agriculture, the control of the land with satellites [10][11][12], the control of data in cloud environments [13][14][15], the Internet of Things (IoT) [16][17][18][19][20], computer vision with the development of imaging systems, machine learning, and algorithms have started to be used intensively in agriculture [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots

Türkler,

Akkan,

Akkan

2023

Sensors

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In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an electronic system can help achieve this objective. In this research, an experimental study was carried out to detect water leaks in the field in order to prevent water losses that can occur in agriculture, where water consumption is the highest. Therefore, in this study, low-cost embedded electronic hardware was developed to detect over-watering by means of normal and thermal camera sensors and to collect the required data, which can be installed on a mobile agricultural robot. For image processing and the diagnosis of abnormal conditions, the collected data were transferred to a personal computer server. Then, software was developed for both the low-cost embedded system and the personal computer to provide a faster detection and decision-making process. The physical and software system developed in this study was designed to provide a water leak detection process that has a minimum response time. For this purpose, mathematical and image processing algorithms were applied to obtain efficient water detection for the conversion of the thermal sensor data into an image, the image size enhancement using interpolation, the combination of normal and thermal images, and the calculation of the image area where water leakage occurs. The field experiments for this developed system were performed manually to observe the good functioning of the system.

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A data-driven bibliometric review on precision irrigation

Cited by 6 publications

References 34 publications

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Autonecrotic Tomato (Solanum lycopersicum L.) Line as a Potential Model for Applications in Proximal Sensing of Biotic and Abiotic Stress

Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots

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