Agricultural Monitoring System Research Articles

Cloud-based Internet of Things Approach for Smart Irrigation System

The agriculture sector is facing numerous challenges such as water scarcity, climate change, and low productivity due to outdated farming practices. In most of the countries, 75% of their GDP depends on the agricultural products. Hence at most care has to be taken to introduce modern technology in the agriculture sector to enhance its productivity and efficiency. This paper presents an IOT Based Smart Agriculture Monitoring System aimed to increase the agricultural productivity through automating and optimizing crop management. The first objective of the proposed system is to supply the water to the crops based on soil moisture and the surrounding temperature. It can be achieved through soil moisture sensor, DHT11 sensor, light sensitive module and Node MCU. All sensors are interfaced with Node MCU. The data collected through sensor will be processed by the processor. Whenever the soil moisture is less and temperature is more, the pump will be automatically switched on for irrigation. Apart from this the collected data from sensor is also transmitted to the cloud through Thingspeak. The same information can be accessed by the farmers to monitor the crops remotely and improve the productivity. By providing farmers with real time data on their crops, the system can help them make informed decisions regarding water and fertilizer usage, pest control, and harvesting times. This, in turn, can lead to increased crop yields, reduced costs, and improved profitability. With the increasing demand for food production and the need to address the challenges of climate change and food security, this project serves as a promising solution for sustainable agriculture.

Soil Health Metric: IoT-based Agriculture Monitoring System

Agriculture has been an essential component of human civilization, experiencing considerable evolution over the centuries. In contemporary society, technological advancements, particularly the Internet of Things (IoT), have emerged as transformative forces, facilitating precision farming and enhancing resource management efficiency. This research examines the development and implementation of an IoT-based agricultural monitoring system. By utilizing components such as sensors, wireless networks, and data analytics, this system offers real-time insights into soil moisture, temperature, humidity, and water levels. The automation will be integrated into optimal resource utilization, minimization of waste, and increased productivity. This paper outlining the operation, parts, and benefits of IoT-based farming systems with a perspective on its transformation of agriculture and the problem of sustainability and its relation to the environment.

Automation in Greenhouse using IoT

This work provides an IoT-based plant monitoring system for avoiding over-exploitation of resources, ensuring compact design with easy installation and the best environment for the plants to grow. This type of agricultural monitoring system provides environmental monitoring services that help the crop grow in good shape. The system offers the service of storing a database about the environment and soil information acquired from a wireless sensor network deployed in the planted area. Furthermore, it enables users to track environmental data regarding planted crops in real-time using any Internet-enabled device. Our project introduces automatic sunlight prevention and soil moisture control based on data received from the moisture and temperature sensors. The proposed work is implemented using a microcontroller, programmed accordingly to measure the temperature and moisture using respective sensors. It is well capable of controlling temperature by operating a fan, controlling a shade for blocking excess sunlight and controlling moisture by operating a watering device through a motor. The potential outcome of the project work can be used for greenhouse

Enhancing global agricultural monitoring system for climate-smart agriculture

Global agricultural monitoring systems face unprecedented challenges due to intensifying climate change. This paper reviews the advancements in existing global agricultural monitoring systems, highlighting deficiencies in addressing extreme weather events, data integration, and real-time analysis. To overcome these limitations, we introduce the Earth System Model-Coupled Global Agricultural Monitoring System (ESM-GAMS), an advanced framework that combines satellite and near-surface remote sensing, artificial intelligence-driven modeling, supercomputing, and crop model to enhance the accuracy and timeliness of crop monitoring and yield predictions under diverse climate scenarios. By integrating multi-source remote sensing data, ESM-GAMS mitigates delays caused by satellite revisit cycles and weather interference, enabling near real-time monitoring with results available at hourly or minute-level intervals. Additionally, the system demonstrated high accuracy in yield simulations under extreme weather, with the improved WOFOST model achieving robust R2 values ranging from 0.55 to 0.77, indicating its reliability in predicting yields across diverse conditions. ESM-GAMS not only enables detailed daily monitoring of crop growth, but also provides early-warning capabilities for extreme weather and its impact on prediction. By optimizing resource allocation, supporting climate resilience, and enabling global data computing, ESM-GAMS represents a further step toward achieving climate-smart agriculture.

Design an Agricultural Soil and Environment Monitoring System Based on IoT

One of the problems farmers face is the inability to make complete, real-time, and accurate observations of their farmland. The system proposed in this paper helps farmers to know the condition of farmland from anywhere and anytime by using a web-based application. The main objective of this prototype is to reduce the failure of the growth process of farming commodities by knowing the conditions inside and outside the soil with a total of 14 parameters. Internet of Things (IoT) technology is used to implement the prototype, which consists of Sensor Panels, Controllers, Message Broker, and Backend Service. All obtained data, created and tested in real-time, are displayed on the application. In addition to real-time data display, the system also includes monitoring history, alerts, and site location management.

Research on Intelligent Agricultural Monitoring and Control System Based on Neural Networks in the Internet of Things Environment

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

Blockchain Enabled Secure Data Transmission With NB‐IoT Deployment in Smart Agriculture Crop Watch

ABSTRACTThe integration of Narrowband Internet of Things (NB‐IoT) and blockchain technology presents a promising solution to address security and efficiency concerns in data transmission within the Internet of Things (IoT). This research focuses on its application in crop Watch, an agricultural monitoring system. The IoT has revolutionized various industries, including agriculture, by facilitating the collection and transmission of extensive data through sensors and devices. However, ensuring secure and efficient data transmission remains a critical challenge. NB‐IoT boasts advantages such as improved coverage in challenging areas, massive connectivity with up to 100 000 links, low energy usage through prolonged discontinuous receipt (eDRX), and cost‐effectiveness. Despite these benefits, NB‐IoT faces security issues, including authentication, anonymity, and fault tolerance. To enhance data security, public key cryptography is proposed to be incorporated into the NB‐IoT framework. Blockchain technology, with its decentralized and immutable nature, can potentially address these security concerns and enhance data accountability. By creating a trust‐less and transparent infrastructure, blockchain complements NB‐IoT's capabilities and provides a robust mechanism for data verification and integrity. This research paper emphasizes the logical integration of NB‐IoT, public key cryptography, and blockchain technology, offering a secure and efficient data transmission framework for agricultural applications like crop Watch. Bridging the gap between data connectivity and security holds promise for advancing agricultural monitoring and driving IoT innovations.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Drone Technology for Precision Agriculture: Advancements and Optimization Strategies

This paper delves into the development and optimization of an intelligent agricultural monitoring system that makes use of drone technology to enhance agricultural productivity and sustainability. With the world's population on the rise and the scarcity of arable land, precision agriculture becomes essential in guaranteeing food security. By harnessing state-of-the-art sensors and imaging technologies, drone technology offers a new and innovative approach to agricultural monitoring. Efficiently collecting vital data on vegetation indices, soil moisture, and crop health. This study seeks to create a state-of-the-art drone-based monitoring system that uses advanced machine learning algorithms and data processing technologies to analyze agricultural data with exceptional precision. It explores the most efficient methods for operating drones, including flight planning and data collection protocols, with the aim of creating a comprehensive agricultural monitoring system. This platform provides the convenience of automating monitoring processes, leading to lower labor costs, improved resource allocation, and a beneficial impact on agricultural modernization. The article delves into the effects of the latest developments in drone technology and the subsequent decrease in costs on the agriculture industry worldwide. It highlights the ways in which these advancements are transforming conventional farming methods.

A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019

Abstract The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency’s WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

The link between the normalized difference vegetation index in major crops and meteorological factors

Normalized difference vegetation index (NDVI) is frequently used in monitoring of meteorological events. The goal of this study was to establish the relationship between the spatial NDVI with air temperature and precipitation amounts in major crops cultivated in the steppe zone of Ukraine, namely, winter wheat, winter rapeseed, grain corn, soybeans, and sunflower. The study included the croplands of eight regions: the Crimea, Kherson, Mykolaiv, Odesa, Zaporizhzhia, Dnipropetrovsk, Kirovohrad, and Kharkiv. The yield data were retrieved from the official bodies of the State Statistical Service of Ukraine. The meteorological data on monthly air temperature and rainfall were retrieved from regional hydrometeorological centers. The NDVI values were retrieved from the GIMMS Global Agricultural Monitoring System, which provides Terrain MODIS NDVI 8-Day smoothed time series with 250 m resolution. The study was performed for the 2021-2022 for winter rapeseed and sunflower; 2017 and 2020 for grain corn; 2022 for winter wheat; and 2017 for soybeans. We found almost no or weak correlation between monthly NDVI values of the studied crops and rainfall amounts. A stronger correlation was found for air temperature, with the greatest values of the correlation coefficient of 0.76, 0.72, –0.72 for sunflower, soybeans, and winter wheat, respectively. The linear regression models, developed to predict NDVI based on the air temperature for the mentioned crops, provided good prediction accuracy with the relative error within 10–20%. The best overall fit and accuracy was produced by the model of sunflower’s NDVI. Only winter crops were observed to have a negative correlation with air temperature, suggesting that the cultivated varieties of these crops are heat-intolerant.

The Application of IoT Technology and Deep Learning in Automated Intelligent Control Systems

In order to accurately monitor the environmental information of agricultural greenhouses, achieve remote automatic control, and improve crop yield, the author proposes an application method of Internet of Things technology in automatic intelligent control systems. This method utilizes IoT technologies such as WSN, Android, and cloud platforms to design an intelligent agricultural greenhouse monitoring system, taking sensors such as soil moisture, lighting, temperature and humidity as examples, and using shading, water spraying, fans, and fill lights as control devices. Design from aspects such as system architecture, perception control system, cloud database, and mobile terminal management system to achieve automatic monitoring of agricultural greenhouse environmental information.Experimental results show that: The data collected by the sensor is compared with the actual monitoring data of the instrument, taking temperature as an example, the errors are all within ±0.5◦C of multiple measurements. Conclusion: The system has the advantages of good scalability, convenient networking, and high cost performance, which makes up for the difficulties in wiring and inconvenient use of the traditional agricultural greenhouse monitoring system, and has high practical value.

BUDDHIMAN KRISHI ANUVADHANAM: A New Hope for Better Agriculture System

The Agriculture Monitoring System addresses pressing challenges in contemporary farming, notably ineffective soil moisture management and persistent threats of animal intrusion. Recent statistics indicate a global agricultural loss of 20% due to suboptimal soil moisture practices and animal-related damages. Existing monitoring systems fall short in providing a holistic solution, resulting in significant economic losses for farmers. In response, the proposed system integrates advanced technologies, including precise soil moisture sensors, infrared (IR) sensors for animal detection, and real-time visual monitoring via an ESP32 camera. Farmers can also monitor and control irrigation pumps and electric shock generators through their smartphones. This comprehensive approach aims to minimize yield losses, optimize resource utilization, and empower farmers with actionable insights for informed decision-making. By bridging critical gaps in agricultural monitoring and protection, the Agriculture Monitoring System emerges as a transformative solution with the potential to revolutionize and fortify global farming practices

Piezoelectric breeze energy harvester with mechanical intelligence mechanism for smart agricultural monitoring systems

In this paper, a piezoelectric breeze energy harvester with a mechanical intelligence mechanism for smart agricultural monitoring systems (G-PBEH) is proposed. Different from the conventional magnetically coupled piezoelectric cantilever beam harvesters where the end magnet is mostly fixed, the G-PBEH has movable magnets in a fixed cylindrical channel. Which could achieve a mechanical intelligence mechanism with the tuned magnets on the shell, contributing to increasing voltage frequency and widening wind bandwidth. The effects of cylindrical channel length (L) and tuned magnet diameter (D) on performance were investigated. The experimental findings reveal that when L is 10 mm and D is 8 mm, the prototype starts at 2 m s−1, and the highest voltage and power are 17.9 V and 944.07 μW (150 kΩ) at 8 m s−1 . Compared to L is 5 mm (magnet fixed), the voltage waveform has a 28.6% increase in the quantity of peaks. Besides, the voltage is larger than 3 V occupying 91.6% of the experimental wind bandwidth. The application experiment demonstrates that the G-PBEH can be used as a reliable power supplier, which can facilitate the progress of smart monitoring systems for simplified greenhouses in remote areas.

Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect.

It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.

Design and Architecture of BenguetFresh: An IoT-Enabled Data-Driven System for Sustainable Agriculture

Agriculture plays a vital role in the Philippines, contributing significantly to its economy and providing livelihoods to a substantial portion of the population. Benguet province, known as the "Salad Bowl of the Philippines" for its favorable climate and fertile soil, exemplifies thriving agriculture-based livelihoods. However, challenges persist in the sector, with the low compliance rate of Philippine Good Agricultural Practices (PhilGAP) certification highlighting the need for innovative solutions to enhance sustainable practices, market access, and overall agricultural quality for Benguet farmers.
 This study presents the design and architecture of BenguetFresh a system designed to integrate data-driven monitoring and management practices with sustainable agriculture, while adhering to PhilGAP standards. Through stakeholder engagement, interviews, and systematic review, specific requirements and challenges of farmers in Benguet were identified and addressed.
 BenguetFresh employs cutting-edge technology, Arduino-based smart agriculture monitoring systems, enabling real-time data collection on environmental factors. Data analytics tools process this data to provide valuable insights, facilitating informed decision-making and resource optimization.
 The scalable architecture ensures efficient data flow and real-time processing, while user-friendly interfaces empower farmers with real-time and historical data visualization and data-driven recommendations. Furthermore, the integration of e-commerce functionality fosters direct connections between farmers and consumers, expanding market reach and improving economic outcomes.
 BenguetFresh's design and architecture serve as a blueprint for implementing data-driven, sustainable agriculture. The potential benefits extend to both farmers and consumers, ensuring high-quality, responsibly produced agricultural products. Embracing cutting-edge technology and industry standards, BenguetFresh paves the way for efficient, sustainable, and prosperous agriculture.

Solar Based Smart Irrigation System

This article presents a novel mobile application utilizing modern information and communication technology for agriculture, centered on the Internet of Things (IoT), embedded systems, and unmanned aerial vehicles (UAVs). The proposed agricultural monitoring system, employing Arduino microcontroller boards, Wi-Fi modules, and environmental sensors, facilitates data collection on temperature, humidity, and soil moisture. UAVs gather environmental data across the farm, enabling cloud-based computation of irrigation requirements for each region. Additionally, the system remotely monitors farm conditions, including water needs, through an Android mobile application, aiding farmers. Results indicate the effectiveness of our IoT-based system in reducing unnecessary water irrigation, aligning with smart agriculture goals. As the global population burgeons, the demand for increased food production escalates. Traditional farming methods necessitate extensive labor and water usage. This paper introduces a compact automated agricultural system prototype. Employing directional guides, the hardware enables precise processes such as seeding, humidity sensing, and watering, ensuring optimal crop growth and economic viability. From seed sowing to watering, the entire system operates autonomously. Testing involved integrating the hardware with a scaled-down plantation model to validate functionality. Keywords: Solar Panels, Arduino Uno, Soil Moisture Sensor, Relay, Lithium Battery, PIR Sensor, TP4058 Module, Gear Motor, LDR Sensor, Submersible Pump, Buzzer.

Smart Irrigation and Pesticides System for Enhancing Harvesting in Agriculture Using IoT

Agriculture plays a vital role in the economy of many countries, serving as a primary source of food. However, traditional farming practices often face challenges such as inefficient water usage, over-reliance on pesticides, and inconsistent monitoring of crop conditions. In recent years, the Internet of Things (IoT) has emerged as a promising technology to address these challenges by providing real-time monitoring and control of agricultural processes. This leads in excessive water use, which has an impact on agricultural output. This integrated system means that farmers only need to perform little manual monitoring. The opening and closing of the reservoir and field gates, as well as the verification of the plant's age and the matching optimal water level necessary for that time of plant age, are all automated. Farmers may use their mobile phones to check the water level in the field since they are continually receiving updates on the field work. The most labour-intensive and time consuming activity in agriculture is water management. In our project, the water level in the agriculture harvest sting is measured, and if it is lower than the required level, the paddy field is automatically irrigated for the water source or tank using a water pump. It can comprehensively monitor air temperature and humidity, light intensity, ultraviolet light and other environmental elements. In specific use, the agricultural production environment monitoring system starts from the actual needs of the plantation zone and is assembled according to the environmental elements it needs to monitor.

A Raspberry Pi-Powered IoT Smart Farming System for Efficient Water Irrigation and Crop Monitoring

Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.