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
收藏
页数:34
相关论文
共 50 条
  • [21] Soil Nutrient Analysis and Automatic Monitoring in Precision Agriculture
    Sharma, Dinesh
    Tomar, Geetam Singh
    COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2025, 56 (02) : 151 - 172
  • [22] Crop Monitoring using IoT for Precision Agriculture
    Shinde, Satyam
    Dhanurkar, Tushar
    Jain, Vinit
    Pal, Vivek
    Taware, Pradeep
    2024 4TH INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND SOCIAL NETWORKING, ICPCSN 2024, 2024, : 706 - 710
  • [23] A Knowledge Model for IoT-Enabled Smart Banking
    Ramphull, Brijesh
    Nagowah, Soulakshmee D.
    JOURNAL OF THE KNOWLEDGE ECONOMY, 2024, 15 (02) : 9174 - 9206
  • [24] Proper soil sample numbers for soil nutrient analysis in precision agriculture
    赵伟
    谢德体
    刘洪斌
    王晓东
    丁声源
    中国生态农业学报(中英文), 2008, (02) : 318 - 322
  • [25] IoT-Enabled Adaptive Watering System With ARIMA-Based Soil Moisture Prediction for Smart Agriculture
    Afzal, Muhammad
    Saeed, Iftikhar Ahmed
    Sohail, Muhammad Noman
    Saad, Mohamad Hanif Md
    Sarker, Mahidur R.
    IEEE ACCESS, 2025, 13 : 27714 - 27728
  • [26] Enhancing precision agriculture through cloud based transformative crop recommendation model
    Singh, Gurpreet
    Sharma, Sandeep
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [27] Security in IoT-enabled smart agriculture: architecture, security solutions and challenges
    Anusha Vangala
    Ashok Kumar Das
    Vinay Chamola
    Valery Korotaev
    Joel J. P. C. Rodrigues
    Cluster Computing, 2023, 26 : 879 - 902
  • [28] IoT-enabled Traffic Analysis: A Case Study
    Wu, Linna
    Li, Huan
    Ren, Feng
    Zhang, Lizhuo
    PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTERNET OF THINGS DESIGN AND IMPLEMENTATION (IOTDI '19), 2019, : 267 - 268
  • [29] Security in IoT-enabled smart agriculture: architecture, security solutions and challenges
    Vangala, Anusha
    Das, Ashok Kumar
    Chamola, Vinay
    Korotaev, Valery
    Rodrigues, Joel J. P. C.
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2023, 26 (02): : 879 - 902
  • [30] Analysis of soil and crop properties for precision agriculture for winter wheat
    Vrindts, E
    Reyniers, M
    Darius, P
    De baerdemaeker, J
    Gilot, M
    Sadaoui, Y
    Frankinet, M
    Hanquet, B
    Destain, MF
    BIOSYSTEMS ENGINEERING, 2003, 85 (02) : 141 - 152