Agricultural Monitoring System Research Articles

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.

Retraction Note: Agricultural product monitoring system supported by cloud computing

Retraction Note: Agricultural product monitoring system supported by cloud computing

Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate.

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

An internet of things based smart agriculture monitoring system using convolution neural network algorithm

Farming is a crucial vocation for survival on this planet because it meets the majority of people's necessities to live. However, as technology developed and the Internet of Things was created, automation (smarter technologies) began to replace old approaches, leading to a broad improvement in all fields. Currently in an automated condition where newer, smarter technologies are being upgraded daily throughout a wide range of industries, including smart homes, waste management, automobiles, industries, farming, health, grids, and more. Farmers go through significant losses as a result of the regular crop destruction caused by local animals like buffaloes, cows, goats, elephants, and others. To protect their fields, farmers have been using animal traps or electric fences. Both animals and humans perish as a result of these countless deaths. Many individuals are giving up farming because of the serious harm that animals inflict on crops. The systems now in use make it challenging to identify the animal species. Consequently, animal detection is made simple and effective by employing the Artificial Intelligence based Convolution Neural Network method. The concept of playing animal-specific sounds is by far the most accurate execution. Rotating cameras are put to good use. The percentage of animals detected by this technique has grown from 55% to 79%.

Retracted: Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System

Retracted: Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System

Enhancing Agricultural Sustainability with Deep Learning: A Case Study of Cauliflower Disease Classification

The pivotal role of sustainable agriculture in ensuring food security and nurturing healthy farming communities is undeniable. Among the numerous challenges encountered in this domain, one key hurdle is the early detection and effective treatment of diseases impacting crops, specifically cauliflower.This research provides an in-depth exploration of the use of advanced DL algorithms to perform efficient identification and classification of cauliflower diseases. The study employed and scrutinized four leading DL models: EfficientNetB3, DenseNet121, VGG19 CNN, and ResNet50, assessing their capabilities based on the accuracy of disease detection.The investigation revealed a standout performer, the EfficientNetB3 model, which demonstrated an exceptional accuracy rate of 98%. The remaining models also displayed commendable performance, with DenseNet121 and VGG19 CNN attaining accuracy rates of 81% and 84%, respectively, while ResNet50 trailed at 78%. The noteworthy performance of the EfficientNetB3 model is indicative of its vast potential to contribute to agricultural sustainability. Its ability to detect and classify cauliflower diseases accurately and promptly allows for early interventions, reducing the risk of extensive crop damage.This study contributes valuable insights to the expanding field of DL applications in agriculture. These findings are expected to guide the development of advanced agricultural monitoring systems and decision-support tools, ultimately fostering a more sustainable and productive agricultural landscape.

Low-cost IoT-based monitoring system for precision agriculture

The increasing impact of climate change on agriculture necessitates advanced monitoring and management of environmental conditions to ensure sustainable agricultural productivity. This paper introduces a cost-effective, Internet of Things (IoT)-based smart monitoring system designed to provide real-time insights into soil moisture levels and weather conditions across various segments of a single agricultural plot. The system comprises autonomous wireless sensor nodes, a comprehensive weather station, and a centralized base station that collectively capture, process, and relay environmental data to a user-friendly mobile application. Our empirical results demonstrate that this system not only facilitates efficient environmental data monitoring and analysis but also empowers farmers with actionable intelligence for timely decision-making. The proposed model showcases a promising avenue for enhancing agricultural resilience and productivity through technology-driven precision farming.

Simultaneous detection of AFB1 and aflD gene by "Y" shaped aptamer fluorescent biosensor based on double quantum dots.

The developed method for simultaneous detection of aflatoxin B1 (AFB1) and aflD genes can effectively monitor from the source and reduce the safety problems and economic losses caused by the production of aflatoxin, which can be of great significance for food safety regulations. In this paper, we constructed a sensitive and convenient fluorescent biosensor to detect AFB1 and aflD genes simultaneously based on fluorescence resonance energy transfer (FRET) between quantum dots (QDs) and a black hole quenching agent. A stable "Y" shaped aptasensor was employed as the detection platform and a double quantum dot labeled DNA fragment was utilized to be the sensing element in this work. When the targets of AFB1 and aflD genes were presented in the solution, the aptamer in the "Y" shaped probe is specifically recognized by the target. At this time, both Si-carbon quantum dots (Si-CDs) and CdTe QDs are far away from the BHQ1 and BHQ3 to recover the fluorescence. The linear range of the prepared fluorescence simultaneous detection method was as wide as 0.5-500ng·mL-1 with detection lines of 0.64ng·mL-1 for AFB1 and 0.5-500nM with detection lines of 0.75nM for aflD genes (3σ/k). This fabricated fluorescent biosensor was further validated in real rice flour and corn flour samples, which also achieved good results. The recoveries were calculated by comparing the known and found amounts of AFB1 which ranged from 88.4 to approximately 115.32% in the rice flour samples and 90.7 ~ 102.58% in the corn flour samples. The recoveries of aflD genes ranged from 84.32 to approximately 109.3% in the rice flour samples and 89.48 ~ 100.99% in the corn flour samples. Therefore, the proposed biosensor can significantly improve food safety and quality control through a simple, fast, and sensitive agricultural product monitoring and detection system.

Advancements in Energy-Harvesting Techniques for Agricultural Applications: A Review

Abstract: Over time, there has been rapid advancement in wireless sensor network (WSN) technologies. These networks have proven to be efficient tools in enhancing agricultural productivity. One significant development in WSNs is the integration of energy harvesting capabilities, allowing them to extract energy from the surrounding environment and utilize it to power wireless Internet of Things (IoT) devices. Energy harvesting involves converting ambient energy from various sources such as solar power, wind, and mechanical vibrations into usable power for devices. This review focuses on exploring the different techniques of energy harvesting for WSNs that can be effectively employed in agricultural monitoring systems. Additionally, it addresses the power consumption issues prevalent in agriculture and aims to identify the most effective strategies for resolving these problems.

STRUCTURAL INVESTIGATION OF HETERO-STRUCTURED CORE-SHELL SILVER-SILICA HYBRID AS A POTENTIAL AGRO-SENSOR MATERIAL

Investigation into the novel synthesis and structural properties of Heterostructured core shell Silver-Silica (AgSiO2) Nanohybrid is reported herein, for possible utilization as humidity sensing material in Agricultural post-harvest humidity monitoring system and greenhouse farming. The Heterostructured nanoparticles (Ag and SiO2) were synthesized via a chemical reduction method. Structural characterizations were done using X-ray Diffraction (XRD), High Resolution Transmission Electron Microscopy (HRTEM), Scanning Electron Microscopy (SEM), UV-Visible spectroscopy, Energy Dispersive X-ray (EDX) spectroscopy and N2 adsorption-desorption techniques. The XRD diffractogram of Ag-SiO2 showed distinct peaks at 2? values of 390, 430, 660 and 790 which was corroborated by the SEM and HRTEM results. EDX convincingly confirmed Ag and SiO2 incorporation into the Ag-SiO2 nanocomposite hetero-structure at 16.7 % and 83.3 % respectively. A Surface Plasmon Resonance absorption peak of 421 nm of Ag was revealed by UV-Visible spectroscopic analysis. An effective and relatively narrow pore size distributions of 3 – 8 nm was revealed by N2 adsorption-desorption for the Ag-SiO2 Nanocomposite. Overall, these properties are promising for effective use as humidity sensing material.

RS Transformer: A Two-Stage Region Proposal Using Swin Transformer for Few-Shot Pest Detection in Automated Agricultural Monitoring Systems

Agriculture is pivotal in national economies, with pest classification significantly influencing food quality and quantity. In recent years, pest classification methods based on deep learning have made progress. However, there are two problems with these methods. One is that there are few multi-scale pest detection algorithms, and they often lack effective global information integration and discriminative feature representation. The other is the lack of high-quality agricultural pest datasets, leading to insufficient training samples. To overcome these two limitations, we propose two methods called RS Transformer (a two-stage region proposal using Swin Transformer) and the Randomly Generated Stable Diffusion Dataset (RGSDD). Firstly, we found that the diffusion model can generate high-resolution images, so we developed a training strategy called the RGSDD, which was used to generate agricultural pest images and was mixed with real datasets for training. Secondly, RS Transformer uses Swin Transformer as the backbone to enhance the ability to extract global features, while reducing the computational burden of the previous Transformer. Finally, we added a region proposal network and ROI Align to form a two-stage training mode. The experimental results on the datasets show that RS Transformer has a better performance than the other models do. The RGSDD helps to improve the training accuracy of the model. Compared with methods of the same type, RS Transformer achieves up to 4.62% of improvement.

Fabrication and investigation of agricultural monitoring system with IoT & AI

Artificial intelligence (AI) can be used in a variety of fields and has the potential to alter how we currently view farming. Due to its emphasis on effectiveness and usability artificial intelligence has the largest impact on agriculture of all industries. We highlight the automation-supporting technologies such as Artificial Intelligence (AI), Machine Learning, and Long-Range (LoRa) technology which provides data integrity and protection. We also offer a structure for smart farming that depends on the location of data processing after a comprehensive investigation of numerous designs. As part of our future study we have divided the unresolved difficulties in smart agriculture into two categories such as networking issues and technology issues. Artificial Intelligence and Machine Learning are examples of technologies whereas the Moderate Resolution Imaging Spectroradiometer satellite and LoRa are used for all network-related jobs. The goal of the research is to deploy a network of sensors throughout agricultural fields to gather real-time information on a variety of environmental factors including temperature, humidity, soil moisture and nutrient levels. The seamless data transmission and communication made possible by these sensors’ integration with Internet of Things technologies. With the use of AI techniques and algorithms the gathered data is examined. The technology may offer practical insights and suggestions for improving agricultural practices because the AI models are trained to spot patterns, correlations, and anomalies in the data. We are also focusing on indoor farming by supplying Ultra Violet radiation and artificial lighting in accordance with plant growth. When a pest assault is detected using AI and LoRa even in poor or no network coverage area and notifies the farmer’s mobile in any part of the world. The irrigation system is put to the test with various plants at various humidity and temperature levels in both dry and typical situations. To keep the water content in those specific regions soil moisture sensors are used.

IoT-Enabled Water Level Monitoring for Smart Farming

Abstract: The rate of population growth in the world is alarming. It is quite difficult to meet the needs of such a large population. Good nutrition is the most fundamental requirement for each human being. The old and conventional farming techniques, however, are proving insufficient for supplying food in large amounts due to the growing population. Fortunately, by utilizing cutting-edge agricultural techniques and smart electronics technology, we can raise efficiency and productivity to higher levels. Additionally, this will guarantee us access to food. An IOT-based smart agriculture monitoring system project using Arduino is presented to improve the effectiveness and productivity of agricultural crops. One of the most crucial aspects of our society is agriculture. Every day, farmers generate food. Water is a key aspect of successful agriculture. Technology has played a crucial role in developing agriculture. The world's largest water user is the agriculture industry. Since water is used so extensively in agriculture, which makes up the majority of the Indian economy, it is disappearing day by day. One answer to this issue is irrigation, as plants are fed with water by drip irrigation. Water is well conserved by irrigation. The agricultural land must be consistently watered while being continuously monitored. In many parts of the world, manual irrigation is still used to deliver water for agriculture.

Assessment & Monitoring of Agricultural Drought Indices Using Remote Sensing Techniques and their Inter-Comparison

Agricultural drought is nothing but the decline in the productivity of crops due to irregularities in the rainfall as well as decrease in the soil moisture, which in turn affects the economy of the nation. As the Indian agriculture is largely dependent on the Monsoon, a slight change in it affects the production as well as the crop yield drastically. The agricultural drought monitoring, assessment as well as management can be done more accurately with the help of geospatial techniques like Remote Sensing. In present study agriculture drought is estimated in vadodara district. The purpose of the study is to analyze the vegetation stress in the Vadodara district with the calculation of NDVI, NDWI, MNDWI, WRI, NDBI and NDSI indices values. The results of NDVI values shows agricultural fields more susceptible to drought. Similarly, decreasing trend was observed in NDWI index which is 22.12% to 19.3% from 2013 to 2018.The builtup index (NDBI) is increasing by 1.85% in last 5 years. The Water ratio index is also showing decreasing trend in study area by 22.35% to 14.29%. The intercomparisions of NDVI and all other indices with rainfall data provides very useful information for agricultural drought monitoring and early warning system for the farmers. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

A Novel Rel-DASA Model for Reliable Data Analysis in IoT Based Smart Agriculture

Agriculture today depends largely on these technologies to supply food for human consumption because to their widespread usage. The Internet of Things bridges the gap by providing farmers with critical tools to predict crop growth, availability of moisture in the soil, soil status, and so on. We describe a comprehensive agricultural monitoring system for accurate crop growth management. This proposed research investigates how supplying farmers with real-time data such as weather forecasts through sensor integration might enhance decision-making. Internet of Things, Agricultural Sensors, Wi-Fi Module, Android App, and Raspberry Pi are all integrated.