Automatic Investigation of Micronutrients and fertilizer dispense System using Microcontroller
27 Jul 2018-Vol. 2018, pp 1737-1739
TL;DR: In this article, the authors proposed design of a system which will detect the soil nutrients and necessary will automatically dispense the fertilizer in the soil, in general situation farmers add fertilizers manually, but the amount may not be proper, inappropriate quantity can harm the life of plant and cut the yield too.
Abstract: Quality of the soil is a measure of soil fertility, observing the nutrients in the soil leads to plant life prediction, if the amount of macro and micronutrients, pH, and water measured then one can predict the fertility and protect plant life. Harvesting process change the soil nutrients and they get depleted hence replenishment is necessary, in this paper we propose design of a system which will detect the soil nutrients and necessary will automatically dispense the fertilizer in the soil, in general situation farmers add fertilizers manually, but the amount may not be proper, inappropriate quantity can harm the life of plant and cut the yield too. The proposed research solution aims to restore the level of phosphorous, potassium & Nitrogen by measuring soil nutrients using a chemical process by using some sensors and adding the proper amount of fertilizers in the soil to support the plant growth and achieve better yield, this automatic detection and dispensing of fertilizers leads to solution for avoiding excess/deficient fertilizers in soil.
Citations
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TL;DR: In this article, the authors developed an aquaponic pond water macronutrient prediction model (wNPK) based on leaf photosynthetic signature predictors using a digital camera, 18 spectro-textural-morphological features were extracted.
Abstract: Crop quality depends dominantly on the nutrients present in its growth media. For precision farming, fertigation is a challenge, especially when dealing with economical and efficiency factors. In this study, the aquaponic pond water macronutrient prediction model (wNPK) was developed based on leaf photosynthetic signature predictors. Aquaphotomics was preliminarily used for correlating physical limnological properties with nitrate, phosphate, potassium concentrations, and the leaf signatures. Using a digital camera, 18 spectro-textural-morphological features were extracted. Neighborhood component analysis (NCA) and ReliefF algorithms selected the spectral components blue, a*, and red minus luma as the most significant as supported by principal component analysis, resulting in low computational cost. A Gravitational Search Algorithm (GSA) was employed to optimize the recurrent neural network (RNN) architecture resulting in higher sensitivity. The hybrid NCA-ReliefF-GSA-RNN (wNPK) predicted NPK with 93.61, 84.03, and 91.39 % accuracy, respectively, besting out other configured feature-based machine learning models. Using wNPK, it was confirmed that potassium helped in accelerating seed germination and nitrogen in promoting chlorophyll intensification, especially on the 6th week after sowing. Phosphate and potassium were the energy and health elements that were consumed in a larger amount at the end of the head development stage. wNPK rules out that macronutrient concentration have a direct resemblance to crop leaf signatures; thus, a leaf is a good indicator of the water quality. The results pointed out that the use of a single camera to measure both water macronutrient concentrations and crop signature at the same time is an innovative, efficient, and economical approach for precision farming.HIGHLIGHTS
Aquaponic pond water nutrient estimation based on leaf photosynthetic signatures
Macronutrient biomarker extraction through UV-Vis-NIR aquaphotomics
Highly accurate macronutrient prediction using hybrid gravitational search and RNN
Potassium promotes seed germination and nitrogen in chlorophyll intensification
Phosphate and potassium are consumed in greater scale during head development
GRAPHICAL ABSTRACT
9 citations
DOI•
07 Oct 2021TL;DR: In this paper, the authors describe the design, installation, and operation of various sensors used to track and manage the environment automatically in greenhouses using Wireless Sensor Network (WSN).
Abstract: A greenhouse is an area with arrangement of components and things that is covered with a kind of materials, like a polyethylene or glass roof or mostly covered with green nets; Since the plants, corps, soil and other materials inside the structure absorbs visible solar radiation rays from the sun, the structure gets heat up dramatically. As a result the greenhouse gases inside the building increases. Many farmers are unable to make good sum from greenhouses because they are unable to control two critical factors: productivity and plant growth. The smart greenhouse monitoring system is designed to assist farmers in overcoming these challenges. The green house monitoring device is powered up by the arduino and Atmega328 microcontrollers and it includes 12v DC fan, LCD display and sensors such as soil moisture sensor, humidity sensor, temperature sensor, light sensor, Light Dependent Resistors(LDR) sensor and a pump. This paper describes the design, installation, and operation of various sensors used to track and manage the environment automatically in greenhouses using Wireless Sensor Network.
1 citations
TL;DR: In this paper , an information system based on different sensing capability, Internet of Things (IoT), and mobile application for soil fertility evaluation is presented. And 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 literature and field research.
Abstract: 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.
1 citations
TL;DR: The proposed best recommendation system recommends crops for farmers to grow based on input from the farmer’s field, such as the temperature, soil, moisture, and nutrient like NPK, pH, and rainfall, in light of all relevant parameters.
Abstract: Agriculture and its related sectors are unquestionably the most important sources of income in rural India. Additionally, having a big impact on the nation's GDP is the agriculture sector. The sector of agriculture is so large, which is great for the nation. The crop production per hectare, however, falls short of international standards. This is one of the most likely causes of the greater rate of suicide among marginal farmers in India. This research proposed best recommendation system. The proposed system recommends crops for farmers to grow based on input from the farmer’s field, such as the temperature, soil, moisture, and nutrient like NPK, pH, and rainfall. Machine learning algorithms allow for optimal crop selection to be made in light of all relevant parameters. three popular machine learning algorithms were tested in this study which includes the Decision Tree, the Random Forest (RF), and the Logistic Regression. The Random Forest among them demonstrated the highest outcomes with 99.32% accuracy.
References
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01 Nov 2016
TL;DR: In this paper, the authors used colorimetry to determine the Nitrogen, Phosphorus and Potassium content and pH of the soil to be cultivated by using the chemicals of the Soil Test Kit in giving nutrient recommendations.
Abstract: The study was about creating a device that can use colorimetry to determine the Nitrogen, Phosphorus and Potassium content and pH of the soil to be cultivated by using the chemicals of the Soil Test Kit in giving nutrient recommendations. Trials were done to produce the actual color reaction of the reactants which served as the color references. The RGB values were extracted using TCS3471 color sensor and an LDR color sensor. The device was tested using three samples each for every nutrient and pH with ten trials each. The benchmark value used was 0.05 to have a 95% level of confidence. The computed value of Chi-Square was equal to 0 using the table for the critical values 0.05 corresponds to the critical value of 3.841. Therefore, computed value was less than the critical value which can be concluded that there was no significant difference between the proposed device reading the soil pH and nutrient content and the human resource reading.
30 citations
01 Jan 2018
TL;DR: This paper presents design of precision agriculture system infrastructure aiming at a multi-parameter monitoring system using wireless sensor network based on low-power Intel's Galileo Gen-2 platform for monitoring, controlling and decision-making support using Internet of Things.
Abstract: This paper presents design of precision agriculture system infrastructure aiming at a multi-parameter monitoring system using wireless sensor network. Proposed infrastructure is based on low-power Intel’s Galileo Gen-2 platform for monitoring, controlling and decision-making support using Internet of Things (IoT). Collection of different farm field parameters is to be done using sensor nodes deployed in the farmland. Each node is connected wirelessly to the base station for the collection of data using wireless transreciever hardware platform. Data is then fed to the personal computer and displayed on screen, e.g. temperature, humidity, sprinkler water flow and soil moisture. From the collected data, decision-making and controlling action can be taken by the use of Internet of Things.
24 citations
01 Aug 2017
TL;DR: This paper focuses on optimize the use of water as needed of chili plants and Internet of Things (IoT) can be used to data analysis for managed and controlled in detail and precisely by Sensor.
Abstract: An agrarian country like Indonesia that has many diverse plants such as chilies that have economic value and very popular as food spices in Indonesia. However the problem is chili plants are very sensitive to several factors such as in soil moisture and weather changes and lack of monitoring. Automatic Water Sprinkle and Monitoring System is based on the problem mentioned before with the incorporation of IoT Technology in automatic water sprinkle and real-time monitoring. This system is designed to replace conventional chili sprinkle to automatic. The system using the moisture sensor to capture current moisture of the soil, and data will be processed in Arduino as microcontroller. This data will determine humidity to open or close valve. This system also uses pH sensor which is used to detect acidity or alkalinity for chili plant and EC sensor to determine the nutrient solution of the soil. The system is also equipped with neutralizing pH and EC automatically. pH sensor and EC sensors will be automated data collection, graphing, and data analysis. The result will be open valve to neutralizing fluid PH and EC. Processed data will be sent to the Web App via Ethernet Shield, Farmers can monitor chili plants in real-time using a smartphone. This paper focuses on optimize the use of water as needed of chili plants and Internet of Things (IoT) can be used to data analysis for managed and controlled in detail and precisely by Sensor.
17 citations
"Automatic Investigation of Micronut..." refers methods in this paper
...Main units of the system are Arduino Mega2560, tcs3200 color Sensor, pH Electrode LCD, Zigbee, and Relay operated Control Valves for fertilizer Dispensing[3]....
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...After successful nutrients identification, we are able to send this data to data acquisition system with the help of Zigbee[2]....
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TL;DR: The results indicated generally that macronutrient levels of phosphates and nitrates were low and below the required minimum required levels for normal plant growth in Kakamega North district as mentioned in this paper.
Abstract: Kakamega North district is situated in Kakamega County in the western region of the Republic of Kenya. It is a major sugarcane producing area. The soils in the area were analyzed for selected macro and micronutrients. Macronutrients analyzed included potassium, sodium, calcium, phosphates and nitrates while the micronutrients analyzed were copper, iron and zinc. The techniques employed were Flame Atomic Absorption Spectrometry, Flame Emission Spectrometry and UV/Visible Spectrophotometry. The soil pH measurement was also taken. The average levels obtained in mg/kg for potassium, sodium, calcium, nitrates and phosphates were 110, 240.8, 540, 71 and 100 respectively. Recommended nutrient range levels in mg/kg as per Kenya Agricultural Research Institute (KARI) are: potassium 1 -100 mg/kg, sodium 20-300mg/kg, calcium 2- 400mg/kg, nitrates above 42 mg/kg and finally for phosphates 2000-5000mg/kg. The results indicate generally that macronutrient levels of phosphates and nitrates were low and below the required minimum required levels for normal plant. Micronutrients were within the limits for normal plant growth. The average soil pH was 5.48 which is acidic and within the limits for normal plant growth.
17 citations
"Automatic Investigation of Micronut..." refers background in this paper
...Main units of the system are Arduino Mega2560, tcs3200 color Sensor, pH Electrode LCD, Zigbee, and Relay operated Control Valves for fertilizer Dispensing[3]....
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...Electrode LCD, Zigbee, and Relay operated Control Valves for fertilizer Dispensing[3]....
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