IoT based Soil Nutrition and Plant Disease Detection System for Smart Agriculture

Suhag, Sashant; Singh, Nidhi Raj; Jadaun, Sanskriti; Johri, Prashant; Shukla, Ayush; Parashar, Nidhi

doi:10.1109/csnt51715.2021.9509719

Cited by 30 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, information is sent through only SMS. The work described in [3,4] the researchers have developed a framework which checks Soil Nutrition based on IoT. In the work Plant Disease detection system is also developd.…”

Section: Literature Reviewmentioning

confidence: 99%

AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield

Joshi-Bag,

Vyas

2023

IJEEI

View full text Add to dashboard Cite

Agriculture is a backbone of any country. Farmers need to test the soil fertility and nutrients present in the soil for proper growth of the crops. In traditional system, the farmers collect soil sample and submit to soil testing labs for testing the soil nutrients and get the soil test reports manually. Farmers based on his experience and the season; decide which crop to be taken in the farm. Based on soil testing reports farmers decide which fertilizers required for the proper growth of the crop. This process is time consuming and human efforts are required and hence crop yield is affected. The recent technologies in cloud storage, wireless sensors, and AI based algorithms are very instrumental in decision making process of crop growth life cycle. Farmers can make use of mechanical automation tools for seeding, watering, supplying fertilizers, crop cutting etc. for proper growth of the crop. However, to observe the crop growth during the entire life cycle of crop farmer has to take lot of efforts to check need of water, any problem of disease to the crop, any specific fertilizers required or not and whether there is a need of harvesting. A proper decision support system is needed for helping the farmers in all such activities. Such support can be provided to a farmer so that he will be well updated about the growth of his crop in the farm. To reduce the human efforts and improve the crop yield, Artificial Intelligence and IOT based soil testing and Crop Recommendation system (AIoTST-CR) is designed and developed. AIoT based handheld soil testing system has pH, Nitrogen, Phosphorous, Potassium and Soil moisture sensing capability. A mobile application is developed to fetch the sensed data from AIoT system. A historical data is inputted to give training to ML models. Machine learning algorithm is used to predict and recommend the crop to be taken. The results show AIoTST-CR which is AIoT based soil testing and crop recommendation system provides effortless and accurate recommendations of crop. Our findings indicate that AIoT based system provides high accuracy, which outperforms existing commonly, used machine learning based crop recommendation systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield

Joshi-Bag,

Vyas

2023

IJEEI

View full text Add to dashboard Cite

show abstract

“…Suhag et al [47] Explained there is no denying that farmers and landowners face challenges due to the everincreasing population. One effective solution is to use new technologies such as AI and IoT.…”

Section: B Soilmentioning

confidence: 99%

“…Topic Solution [47] Soil Nutrition [51] Soil Classification [48] Soil Detection System [49] Soil Humidity Sensing [52], [53] Soil Moisture Prediction and Forecast [54] Soil Moisture Evaluation [55], [56] Soil Prediction [57] Soil Estimate Soil Moisture [50] Soil Analysis Kwok and Sun [59], On technology in ML can be used for learning and is very important in agriculture. In recent years, many studies have taken advantage of machine learning as part of AI.…”

Section: Referencesmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

View full text Add to dashboard Cite

Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

show abstract

“…Additionally, it can reveal hidden patterns and relationships in data [21]. The outcomes of the data analysis can be applied to the detection of plant diseases (Suhag et al, 2021), identify and manage weeds [23], [24], also smart irrigation [25], [26]. In addition, it can operate a soil fertility monitoring system [22], [27].…”

Section: Introductionmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

show abstract

IoT based Soil Nutrition and Plant Disease Detection System for Smart Agriculture

Cited by 30 publications

References 13 publications

AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield

AIoTST-CR : AIoT Based Soil Testing and Crop Recommendation to Improve Yield

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Contact Info

Product

Resources

About