Automated Agricultural FieldAnalysis and Monitoring System Using IOT

Kajol, R; K, Akshay Kashyap; G, Keerthan Kumar T

doi:10.5815/ijieeb.2018.02.03

Cited by 17 publications

(6 citation statements)

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“…While grading the soil based on its properties, Linear regression proves to be an efficient algorithm [3] with very less root mean squared error which is a metric for measuring accuracy in case of regression problem. In case of crop prediction, Random Forest proves to be a better classifier as compared to Gaussian Naïve Bayes [7] and Support Vector Machine [6], [14], [15]. This model helps at predicting soil fertility class using various algorithms like decision tree algorithm where large amount of data is present.…”

Section: Results Analysismentioning

confidence: 99%

“…This eventually generates a model which has higher accuracy in wide diversity [4], [11]. In this algorithm only selective features are taken into account for the splitting of a node [14], [16]. The trees can be made more random, by using random thresholds for the feature set rather than searching for the best thresholds possible.…”

Section: Module-2 Random Forest Classifiermentioning

confidence: 99%

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Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning

Shubha¹,

Sushma²

2019

IJITEE

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n society the population is increasing at a high rate, people are not aware of the advancement of technologies. Machine learning can be used to increase the crop yield and quality of crops in the agriculture sector. In this project we propose a machine learning based solution for the analysis of the important soil properties and based on that we are dealing with the Grading of the Soil and Prediction of Crops suitable to the land. The various soil nutrient EC (Electrical Conductivity), pH (Power of Hydrogen), OC (Organic Carbon), etc. are the feature variables, whereas the grade of the particular soil based on its nutrient content is the target variable. Dataset is preprocessed and regression algorithm is applied and RMSE (Root Mean Square Error) is calculated for predicting rank of soil and we applied various Classification Algorithm for crop recommendation and found that Random Forest has the highest accuracy score.

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Section: Results Analysismentioning

confidence: 99%

Section: Module-2 Random Forest Classifiermentioning

confidence: 99%

Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning

Shubha¹,

Sushma²

2019

IJITEE

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“…Kajol and Kashyap [23] proposed an Automated Agricultural Field Analysis and Monitoring System Using IoT. In this system, the moisture sensor is used to measure the moisture content from the soil at every 100 m distance and there is camera records the video.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the works in [12][13][14][15][16][17][18][19][20][21][22][23] have developed the monitoring and controlling system for farming and agriculture using various method. The project that is proposed for this study is to monitor the chili plants using IoT.…”

Section: Introductionmentioning

confidence: 99%

Smart Agricultural Monitoring System using IoT Application for Chili Plants

Nurhadirah Azman,

Darmawaty Mohd Ali,

Yusmardiah Yusuf

2023

ARASET

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The use of IoT applications for agricultural in Malaysia is still low. The farmers are mainly using the traditional method compared to modern agriculture by using advanced technology system like IoT application. However, the use of traditional agriculture requires a lot of work and use of labour. This will result their agricultural system become less efficient and quality of production becoming low. Thus, development of system for monitoring and automation are needed to help farmers to manage their agriculture. This paper focused on development of smart agriculture for monitoring and automation system on chili plants using IoT application. The system is developed using Arduino as microcontroller, soil moisture sensor, DHT11 sensor (temperature and humidity), ultrasonic sensor, servo motor, water pump and ESP8266 (Wi-Fi Module). The Wi-Fi module provides internet access to the local network which allows the system to connect to the system and the mobile phone. Soil moisture sensor used to measure the moisture of soil content. DHT11 sensor was used to measure the temperature and humidity of surrounding area of plants. Ultrasonic sensor was used to measure the height of plants. Servo motor is controlled by automation system via Remote XY Apps to provide the fertilizer to the plants. The water pump also can be controlled by the Remote XY apps as to water the plants. Then, data that was collected from the sensors was sent to user via Remote XY apps where user can control the system that is to turn on the servo motor and water pump to fertilize and water to the plants. Therefore, this system can help farmers to manage their agriculture efficiently and improve their quality and quantity of crops.

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“…The imaging system uses k-means color clustering and blob counting algorithm to automatically count the insect on the trap sheet. In another case study [13], IoT enabled smart farm field management scheme was proposed to continuously monitor crop growth, detect insects on the farm, and find suitable pesticide for control crop pests. The automated remote imaging system was proposed by Dusty et al [14] for crop protection where Spensa Z-Trap, ADAMA trap view, and DPIRD moth trap module were used to monitor insect populations of farm field remotely.…”

Section: Introductionmentioning

confidence: 99%

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Ramalingam

Mohan

Pookkuttath

et al. 2020

Sensors

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Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

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Automated Agricultural FieldAnalysis and Monitoring System Using IOT

Cited by 17 publications

References 0 publications

Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning

Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning

Smart Agricultural Monitoring System using IoT Application for Chili Plants

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

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