IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture

被引:23
作者
Senapaty, Murali Krishna [1 ]
Ray, Abhishek [1 ]
Padhy, Neelamadhab [2 ]
机构
[1] Kalinga Inst Ind Technol, Sch Comp Engn, Bhubaneswar 751024, India
[2] GIET Univ, Sch Engn, Gunupur 765022, India
关键词
Internet of Things; sensors; soil nutrients; pH value; precision agriculture; crop recommendation; machine learning; FLY OPTIMIZATION ALGORITHM; YIELD PREDICTION; BIG DATA; WEATHER DATA; CLASSIFICATION;
D O I
10.3390/computers12030061
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Healthy and sufficient crop and food production are very much essential for everyone as the population is increasing globally. The production of crops affects the economy of a country to a great extent. In agriculture, observing the soil, weather, and water availability and, based on these factors, selecting an appropriate crop, finding the availability of seeds, analysing crop demand in the market, and having knowledge of crop cultivation are important. At present, many advancements have been made in recent times, starting from crop selection to crop cutting. Mainly, the roles of the Internet of Things, cloud computing, and machine learning tools help a farmer to analyse and make better decisions in each stage of cultivation. Once suitable crop seeds are chosen, the farmer shall proceed with seeding, monitoring crop growth, disease detection, finding the ripening stage of the crop, and then crop cutting. The main objective is to provide a continuous support system to a farmer so that he can obtain regular inputs about his field and crop. Additionally, he should be able to make proper decisions at each stage of farming. Artificial intelligence, machine learning, the cloud, sensors, and other automated devices shall be included in the decision support system so that it will provide the right information within a short time span. By using the support system, a farmer will be able to take decisive measures without fully depending on the local agriculture offices. We have proposed an IoT-enabled soil nutrient classification and crop recommendation (IoTSNA-CR) model to recommend crops. The model helps to minimise the use of fertilisers in soil so as to maximise productivity. The proposed model consists of phases, such as data collection using IoT sensors from cultivation lands, storing this real-time data into cloud memory services, accessing this cloud data using an Android application, and then pre-processing and periodic analysis of it using different learning techniques. A sensory system was prepared with optimised cost that contains different sensors, such as a soil temperature sensor, a soil moisture sensor, a water level indicator, a pH sensor, a GPS sensor, and a colour sensor, along with an Arduino UNO board. This sensory system allowed us to collect moisture, temperature, water level, soil NPK colour values, date, time, longitude, and latitude. The studies have revealed that the Agrinex NPK soil testing tablets should be applied to a soil sample, and then the soil colour can be sensed using an LDR colour sensor to predict the phosphorus (P), nitrogen (N), and potassium (K) values. These collected data together were stored in Firebase cloud storage media. Then, an Android application was developed to fetch and analyse the data from the Firebase cloud service from time to time by a farmer. In this study, a novel approach was identified via the hybridisation of algorithms. We have developed an algorithm using a multi-class support vector machine with a directed acyclic graph and optimised it using the fruit fly optimisation method (MSVM-DAG-FFO). The highest accuracy rate of this algorithm is 0.973, compared to 0.932 for SVM, 0.922 for SVM kernel, and 0.914 for decision tree. It has been observed that the overall performance of the proposed algorithm in terms of accuracy, recall, precision, and F-Score is high compared to other methods. The IoTSNA-CR device allows the farmer to maintain his field soil information easily in the cloud service using his own mobile with minimum knowledge. Additionally, it reduces the expenditure to balance the soil minerals and increases productivity.
引用
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页数:34
相关论文
共 57 条
  • [1] Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR)
    Abrougui, Khaoula
    Gabsi, Karim
    Mercatoris, Benoit
    Khemis, Chiheb
    Amami, Roua
    Chehaibi, Sayed
    [J]. SOIL & TILLAGE RESEARCH, 2019, 190 : 202 - 208
  • [2] The role of big data analytics in Internet of Things
    Ahmed, Ejaz
    Yaqoob, Ibrar
    Hashem, Ibrahim Abaker Targio
    Khan, Imran
    Ahmed, Abdelmuttlib Ibrahim Abdalla
    Imran, Muhammad
    Vasilakos, Athanasios V.
    [J]. COMPUTER NETWORKS, 2017, 129 : 459 - 471
  • [3] A nutrient recommendation system for soil fertilization based on evolutionary computation
    Ahmed, Usman
    Lin, Jerry Chun-Wei
    Srivastava, Gautam
    Djenouri, Youcef
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 189
  • [4] Precision agriculture using IoT data analytics and machine learning
    Akhter, Ravesa
    Sofi, Shabir Ahmad
    [J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2022, 34 (08) : 5602 - 5618
  • [5] Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks
    Alfred, Rayner
    Obit, Joe Henry
    Chin, Christie Pei-Yee
    Haviluddin, Haviluddin
    Lim, Yuto
    [J]. IEEE ACCESS, 2021, 9 : 50358 - 50380
  • [6] Ali S.M., 2021, Revista Geintec-Gestao Inovacao E Tecnologias, V11, P5735
  • [7] RETRACTED: Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease (Retracted Article)
    Ansari, Arshiya S.
    Jawarneh, Malik
    Ritonga, Mahyudin
    Jamwal, Pragti
    Mohammadi, Mohammad Sajid
    Veluri, Ravi Kishore
    Kumar, Virendra
    Shah, Mohd Asif
    [J]. JOURNAL OF FOOD QUALITY, 2022, 2022
  • [8] Optical Oxygen Micro- and Nanosensors for Plant Applications
    Ast, Cindy
    Schmaelzlin, Elmar
    Loehmannsroeben, Hans-Gerd
    van Dongen, Joost T.
    [J]. SENSORS, 2012, 12 (06): : 7015 - 7032
  • [9] Balakrishnan N., 2016, International Journal of Computer Science and Software Engineering, V5, P148
  • [10] Big Data and AI Revolution in Precision Agriculture: Survey and Challenges
    Bhat, Showkat Ahmad
    Huang, Nen-Fu
    [J]. IEEE ACCESS, 2021, 9 : 110209 - 110222