Maximising Value of Frugal Soil Moisture Sensors for Precision Agriculture Applications

Jain, Prachin; Choudhury, Swagatam Bose; Bhatt, Prakruti; Sarangi, Sanat; Pappula, Srinivasu

doi:10.1109/ai4g50087.2020.9311008

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Time series data, capturing sequential observations over time intervals, enable researchers to analyze temporal trends and patterns crucial for agricultural management. Leveraging time series data, studies such as those by [171,210] forecast environmental parameters like temperature and water availability to optimize irrigation scheduling and enhance crop productivity. Furthermore, Ref.…”

Section: Time Series Datamentioning

confidence: 99%

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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Section: Time Series Datamentioning

confidence: 99%

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Soil moisture is also predicted with Boosted Regression Trees (BRT) from UAV-taken images [121] and with ANN in [122]. Health and condition of soil moisture sensors have been predicted using SVM along with the stage of the degradation by using Naive Bayes classification [123]. Autoencoder and SVM have been used to predict the soil salinity from satellite images [124].…”

Section: Soil Managementmentioning

confidence: 99%

Everything You wanted to Know about Smart Agriculture

Mitra¹,

Vangipuram²,

Bapatla³

et al. 2022

Preprint

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The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups -from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.

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“…Soil is a complex resource with many outside interactions. MLAs study evaporation processes, soil moisture and temperature to understand the dynamics of ecosystems and consequences [12].…”

Section: Soil Managementmentioning

confidence: 99%

Smart and Sustainable Agriculture

Maudoux

Boumerdassi

2021

Smart and Sustainable Agriculture

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Last decade has seen the emerging concept of Smart and Sustainable Agriculture that makes farming more efficient by minimizing environmental impacts. Behind this evolution, we find the scientific concept of Machine Learning. Nowadays, machine learning is everywhere throughout the whole growing and harvesting cycle.Many algorithms are used for predicting when seeds must be planted. Then, data analyses are conducted to prepare soils and determine seeds breeding and how much water is required. Finally, fully automated harvest is planned and performed by robots or unmanned vehicles with the help of computer vision. To reach these amazing results, many algorithms have been developed and implemented.This paper presents how machine learning helps farmers to increase performances, reduce costs and limit environmental impacts of human activities. Then, we describe basic concepts and the algorithms that compose the underlying engine of machine learning techniques. In the last parts we explore datasets and tools used in researches to provide cutting-edge solutions.Index terms -Smart and Sustainable Agriculture, Machine Learning, Datasets, Supervised and Unsupervised Algorithms, Practical applications I -INTRODUCTIONSmart farming is a concept of agriculture management based on information and communication technologies implemented to increase products quantity and quality [1]. Different kind of technologies can be found behind this concept: (i) Sensors to scan soils or scale water, light, humidity or temperature (ii) Software for specific farm applications (iii) Communication technologies like cellular network (iv) Positioning by GPS (v) Hardware and software systems that enable IoT-based solutions and automation like drones [2] (vi) Data analytics to make decision and predict outcomes.All these technologies integrated on farms allow farm-holders making farming processes data-driven and data-enabled. This emerging concept makes agriculture more efficient. Next step is now to reach a sustainable agriculture with the help of high-precision algorithms. The mechanism that drives it is machine learning which is the scientific approach that provides machines the ability to learn by themselves [3]. It has emerged with the help of data sciences, computing progresses and the ability to collect then store more and more data.The rest of this article is organized as follows. Section II presents an overview of ML applications in employed by smart agriculture. In section III, we expose main concepts behind machine learning. Sections IV and V describe with details some supervised and unsupervised algorithms respectively. In section VII, we present some widely employed tools and detail Weka. Then, some common metrics for evaluating approach performances are defined. Section VIII concludes this paper. II -APPLICATIONSFrom smart farming to sustainable agriculture, Machine Learning Algorithms or MLAs are mechanisms behind this evolution. The most popular models deployed in agriculture are ANN which stands for Artificial Neural Networks and Sup...

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Maximising Value of Frugal Soil Moisture Sensors for Precision Agriculture Applications

Cited by 8 publications

References 11 publications

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Everything You wanted to Know about Smart Agriculture

Smart and Sustainable Agriculture

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