Detection of nitrogen, phosphorus, and potassium (NPK) nutrients of soil using optical transducer

Masrie, Marianah; Rosman, Mohamad Syamim Aizuddin; Sam, Rosidah; Janin, Zuriati

doi:10.1109/icsima.2017.8312001

Cited by 63 publications

(18 citation statements)

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“…There are different technologies for soil N, P, and K quantification, such as ion-selective membrane (ISM)-based electrochemical sensors, enzyme-based biosensors, molecular-imprinted polymers (MIP)-based biosensing approaches, electro-reduction of nitrate to ammonium ion on copper-based electrodes [ 32 ], fluorescence-based sensors, tamer-based sensors, on-the-go spectroscopy, and electrophoresis-based methods [ 22 ]. Optical or colorimetric sensor [ 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], conductivity sensors [ 30 , 44 , 45 ], and electrochemical sensor [ 46 , 47 , 48 , 49 ] were used for N, P, and K quantification as the sensing layer of an information system based on Internet of Things technology. The proposed sensing devices for IoT-based information systems for soil nutrients assessment have several limitations.…”

Section: Information System For Soil Nutrients Assessmentmentioning

confidence: 99%

IoT-Based Systems for Soil Nutrients Assessment in Horticulture

Postolache

Sebastião

Viegas

et al. 2022

Sensors

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Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.

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Section: Information System For Soil Nutrients Assessmentmentioning

confidence: 99%

IoT-Based Systems for Soil Nutrients Assessment in Horticulture

Postolache

Sebastião

Viegas

et al. 2022

Sensors

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“…to grow a plant. Later, another study [13] proposed and designed an optical transducer to measure soil nutrients such as Nitrogen, Phosphorus and Potassium, and these nutrients are called primary nutrients. Soil nutrient not only depends on primary nutrients and also having macronutrients and micronutrients [14].…”

Section: Machine Learningmentioning

confidence: 99%

A Review on the Role of Machine Learning in Agriculture

Veeragandham

Santhi

2020

SCPE

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Machine learning is a promising domain which is widely used now a days in the field of agriculture. The availability of manpower for agriculture is not enough and skill full farmers are less. Understanding the situation of the crop is not that much easy to detect and prevent the diseases in the crop. It is also widely employed in various agricultural fields such as topsoil management, yield management, water management, disease management and climate conditions. The machine learning models facilitate very fast and optimal decisions. The model of machine learning involves with training and testing to predict the accuracy of the result. The use of machine learning in agriculture helps to increase the productivity and better management on soil classification, disease detection, species management, water management, yield prediction, crop quality and weed detection. This article aims at providing detailed information on various machine learning approaches proposed in the past five years by emphasizing the advantage and disadvantages. It also compares different machine learning algorithms used in the modern agricultural field.

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“…Masrie et al [1] proposed a system for detecting the presence of Nitrogen, Phosphorous and Potassium of the soil using optical transducer. It contains of three LEDs that acts as a light source and uses a photodiode as a light detector.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent System for Monitoring and Managing Agricultural Fields

2019

IJRTE

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Agriculture is a primary concern of every developing nation to maintain their GDP. But most of the processes involved in agricultural sector are still orthodox and inefficient. Though the technology is improving day by day, it is high time to incorporate technology innovations into the field of agriculture for better productivity and resource utilization. We propose an intelligent farming technology with the aid of Internet of Things (IoT) to make various farming techniques smarter and efficient. Various sensors were deployed in the fields to remotely monitor the parameters of soil, water and air. The sensed information is fed to an Android app and it will process the data and alert the farmer if any unusual events occur. Experimental results prove that our model can be used for efficient utilization of agricultural resources like water and fertilizers and thereby improve the productivity and profit.

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Detection of nitrogen, phosphorus, and potassium (NPK) nutrients of soil using optical transducer

Cited by 63 publications

References 1 publication

IoT-Based Systems for Soil Nutrients Assessment in Horticulture

IoT-Based Systems for Soil Nutrients Assessment in Horticulture

A Review on the Role of Machine Learning in Agriculture

An Intelligent System for Monitoring and Managing Agricultural Fields

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