ML based sustainable precision agriculture: A future generation perspective

Priya, Rashmi; Ramesh, Dharavath

doi:10.1016/j.suscom.2020.100439

Cited by 21 publications

(41 citation statements)

References 53 publications

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“…To increase production, Balducci (2018) analyzes environmental data such as climate, humidity, and wind along with production and structural data like type of soil and land extension [36]. Priya (2018Priya ( , 2020 presents a precision farming model to suggest to farmers what crops must be planted in terms of field conditions [37], [38]. Shelestov (2020) does the same, but using data from satellite images [39].…”

Section: Cropsmentioning

confidence: 99%

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Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Cravero¹,

Pardo²,

Sepúlveda³

et al. 2022

Preprint

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Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

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Section: Cropsmentioning

confidence: 99%

“…NN are a good choice for work with big data sets because they have great flexibility to adapt to them, reducing the error produced by adjusting the weights and biases of each neuron based on the data with which it is trained [38]. In Saggi (2019), NN were implemented their performance compared to other ML techniques [49].…”

Section: Neural Networkmentioning

confidence: 99%

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Cravero¹,

Pardo²,

Sepúlveda³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, agribusiness can better monitor the status of crops and receive suggestions to help reduce pollution and pesticide use. These technologies not only help create more sustainable agriculture but also make it more productive and increase profits [4]. Except for wireless sensor networks, remote sensing technology [5], information systems [6], and unmanned aerial vehicles [7] all need the support of machine learning methods to contribute to a smart agriculture system.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

et al. 2021

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With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.

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“…PA covers all aspects of farmland management through sensors and advanced information technology techniques such as field tracking through UAVs [4] as well as modeling spatial and temporal crop yield predictions through machine learning [11]. Wireless sensor networks (WSN) are typically deployed [3] to aggregate the data from the distributed sensors to a single central location such as a cloud storage for further processing using cloud computation platforms.…”

Section: Introductionmentioning

confidence: 99%

Towards Dense and Scalable Soil Sensing Through Low-Cost WiFi Sensing Networks

Hernandez

Erdağ

Bulut

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Precision agriculture uses precise sensor data collected throughout farmland to give farmers better insight into their land, allowing for greater crop yields and reduced resource usage. However, existing solutions require high hardware costs thus limiting large scale deployments. To address that, we propose a low-cost and scalable solution for sensing physical attributes of soil using IoT based WiFi sensing devices. By understanding variations in WiFi radio signals with channel state information (CSI) and machine learning models, we evaluate the proposed soil sensing system through experiments on physical soil traits such as soil moisture content, soil texture and position. Moreover, we also demonstrate how a mesh network of WiFi sensing devices allows us to predict the physical traits of the soil in the area between each pair of sensors, allowing for an increase in sensing area coverage as nodes are added.

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ML based sustainable precision agriculture: A future generation perspective

Cited by 21 publications

References 53 publications

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

Towards Dense and Scalable Soil Sensing Through Low-Cost WiFi Sensing Networks

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