Virtual Farmer: Real Time Crop Prediction and Automatic Irrigation System

Savla, Dev V.; Parab, Amogh N.; Kekre, Kaustubh Y.; Gala, Jay; Ramchandra, S.; Sonawane, Pankaj A.

doi:10.1109/icccnt49239.2020.9225686

Cited by 2 publications

(2 citation statements)

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“…Simple regression algorithms -include linear, Extreme Gradient Boosting (XGBoost), and Stacked regressors-has been utelised to estimate the crop yield [228][229][230]. Decision tree, K-nearest neighbors (KNN), random forest, Naïve Bayes Classifier, and support vector machine (SVM) are simple classifiers used to predict the most suitable crop for a region [210,[231][232][233][234][235][236]. The simplicity of those algorithms enable presenting relationship between data with out demanding extensive computation power, hence can be employed at the edge.…”

Section: E Data Analysis 1) Machine Learning Techniquesmentioning

confidence: 99%

“…The second is using designated controllers that directly connect to the actuators. Relays [96,142,207,233,267,271,279,292,[297][298][299][300][301][302], transistors [303], and motor drivers [205,261] are examples of blocks that operate in conjunction with the microcontroller. An alternative arrangement is replacing the microcontroller and the additional hardware with a programable logic controller (PLC) [272,286,304].…”

Section: B Autonomous Systemsmentioning

confidence: 99%

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Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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