Linear Regression Based Crop Suggestive System for Local Pakistani Farmers

Das, Bhagwan; Ali, Syed Mazhar; Shaikh, Muhammad Zakir; Chandio, Abdul Fattah; Rahu, Mashtaque Ahmed; Pabani, Jitander Kumar; Khalil, Mujeeb Ur Rehman

doi:10.1109/gcwot57803.2023.10064668

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…For example, a thermometer that senses the temperature of a room and communicates with a heater to attain a preset temperature, without the need for a connection to other smart appliances in the home. The capacity to wirelessly interface with other devices via Bluetooth or Wi-Fi abilities can strengthen the smart designation [28]- [29].…”

Section: Overview Of the Iot And Smart Sensing Technologymentioning

confidence: 99%

“…It supports access to mediums for a diverse range of consumers, regardless of scientific training, particularly in industrialized countries. This can be accomplished by making difficult concepts as comprehensible as possible [28]. The WATERMED 4.0 initiative of the PRIMA Foundation7 is a recent project aimed at refining the quality and protection of Mediterranean cultivation in partially arid areas.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Jatoi,

Rahu,

Karim

et al. 2023

JAET

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Water is one of the key elements involved in maintaining the quality of life. It is essential in the field of agriculture for food production and livestock farming. Water quality is continuously declining as a consequence of the growing trend of urbanization and industrialization, which can harm the environment and human health. In recent years, several researchers in the field of agriculture have shown advancements in techniques with the implementation of the Internet of Things (IoT) Artificial Intelligence (AI) machine learning (ML) and smart technologies. These advancements aim at improving water usage and enhancing the quality and quantity of different crops, with the ability to lower analysis costs, time and facilitate the achievement of management results. This paper outlines an overview of recent studies on water control and management using the IoT and ML models in the field of agriculture. Thus, new technologies for analyzing water quality metrics like pH, temperature, color, turbidity, Total Dissolved Solids (TDS), salinity, and nitrogen are described. It also describes few exposed contests used to draw related study suggestions in the future, such as the use of advanced intelligent tools and strategies for water quality assessment and management in the agriculture sector.

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Section: Overview Of the Iot And Smart Sensing Technologymentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Jatoi,

Rahu,

Karim

et al. 2023

JAET

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“…The ultimate goal of intelligent farming is to lower input costs while keeping the same level of output quality. When a large amount of pesticide or fertilizer is administered at once, the entire field is treated as one unit [3]. Moreover, fresh water is a crucial natural resource for all ecosystems to survive.…”

Section: Introductionmentioning

confidence: 99%

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Rahu,

Shaikh,

Karim

et al. 2024

Mehran Univ. res. j. eng. technol.

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All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.

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Deep learning based concurrent excited gated recurrent unit for crop recommendation based on soil and climatic conditions

Venkatanaresh,

Kullayamma

2024

Multimed Tools Appl

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Linear Regression Based Crop Suggestive System for Local Pakistani Farmers

Cited by 3 publications

References 10 publications

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Deep learning based concurrent excited gated recurrent unit for crop recommendation based on soil and climatic conditions

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