Intelligent Agricultural System Research Articles

AGROTECH: A Smart Agricultural System Using IOT and ML

Agriculture remains one of the most important sectors that support human survival which supplies basic needs, a source of income for many dependants' community. Despite these advantages, resource scarcity, adverse environmental conditions and pest infestations remain serious threats to crop products. To mitigate these concerns, we present a smart agriculture system which uses the most modern technologies like IoT (Internet of Things) machine learning (ML) and sensors with automation. The system consists of an intelligent irrigation mechanism for efficient utilization of water, an animal detection system used to trace cattle, and a Light Dependent Resistor (LDR) Sensor joined with buzzer for early detection of any global environmental changes. Additionally, the integration of plant disease detection capabilities further enhances the system's effectiveness. By employing various sensors to collect data on environmental factors, including moisture and temperature, the proposed framework enables timely decision-making for crop management. The aim is to provide farmers with actionable insights, ensuring food security while minimizing resource consumption and economic losses. This review paper discusses the potential benefits and implementation strategies of such an intelligent agricultural system, emphasizing the need for innovation in agricultural practices to meet the growing demands of an increasing population

Assessment of Farmers’ Acceptance of Intelligent Agriculture System Using Technology Acceptance Model

Assessment of Farmers’ Acceptance of Intelligent Agriculture System Using Technology Acceptance Model

The Smart Agriculture System Using IOT and ML

Numerous countries have abundant resources, including land, rivers, groundwater, the environment, with agriculture serving as the primary source of income for many people in country. Nonetheless, resource shortage has grown in recent decades, particularly for groundwater and river water, so remote control smart irrigation system is developed to avoid access use and loss of water in the agriculture. A lack of information about the best use of available resources leads to increased resource consumption and worse crop yields. Choosing crops that are unsuited for the soil or climate reduces both quality and quantity of crop. As a result, system that recommends appropriate crops and fertilizers based on soil NPK values, soil colour, Season, PH, Rainfall, temperature etc has been developed. Fires are one of the most destructive global disasters, thus early detection is necessary to avoid the damages in agriculture. To reduce losses, an automated system capable of early fire detection through alarm systems and prompt extinguishing procedures through the roof structure pipeline in the farm is developed. Another use of that same pipeline is for spraying the pesticides and fertilizers. A potential resolution to address all these challenges involves the development and implementation of an intelligent agricultural system integrating Internet of Things (IOT) and Machine Learning technologies.

Intelligent agriculture system using low energy and based on the use of the internet of things

The field of smart agriculture is ranked among the top areas that uses the internet of things (IoT), whose goal is to increase the quantity and quality of agricultural productivity. The aim of this work is to realize a new device that will be cost-effective, reliable, and autonomous using a solar panel to provide electricity in large-scale agricultural fields, ESP32 to interconnect IoT sensors and the long range (LoRa) data transmission protocol to guarantee connectivity in places where there is no internet, whose objective is to monitor and irrigate agricultural fields only when there is a need for water. The data received by the sensors is sent to mobile app users via the Blynk cloud. The performance of our new approach is measured in terms of energy savings. This new model of irrigation and smart monitoring will improve the efficiency of farming techniques.

Research On Data Analysis and Decision Support of Artificial Intelligence Technology in Intelligent Agricultural System

Research On Data Analysis and Decision Support of Artificial Intelligence Technology in Intelligent Agricultural System

The support and contribution of mobile technologies and applications to agriculture

Agricultural management is based on information, so information about seeds, water and soil moisture, nutrients, plant protection, market of agricultural products, etc., is critical success factor for agriculture. This information must reach the end users or farmers, in order to use the potential for improving agricultural production and business. In this context, information-intensive and precision agriculture techniques based on knowledge and information and communication technologies (ICTs) are essence of contemporary agriculture. Accordingly, farmers could achieve the benefits of the Internet, mobile technologies and other ICTs that provide information services relevant to the management of agricultural production and business. This paper is a contribution to the research of the use of mobile technologies in agriculture with special reference to mobile applications in agriculture and intelligent agricultural system based on mobile and wireless technologies.

Applying Machine Learning to Maximize Agricultural Yield to Handle the Food Crisis and Sustainable Growth

The intelligent agriculture system is a farming-based project, and it will suggest the best crops in the region and maximum yield. Thus, it will affect all the stakeholders related to farming. It may use various technologies such as big data and ML (machine learning). These technologies will help us in fetching the data to train it according to the needs. The agricultural sector also has a significant impact on the country's GDP (gross domestic product). India is rich in the area of agriculture, but the yields per hectare are exceptionally low as compared to the land. The business logic in Python uses machine learning techniques to predict the most suitable crops in the forecasted weather and soil conditions at a specified location. The proposed system will integrate the data obtained from the weather department and by applying machine learning algorithms: Naïve Bayes (polynomial) and support vector machine (SVM) and unsupervised machine learning algorithms like k-means clustering multiple linear regression for weather and environmental conditions are made.

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms.

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

Smart Agriculture Applications Using Deep Learning Technologies: A Survey

Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people’s dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and results with accuracy. We concluded in this survey that although CNN provides better results, it lacks in early detection of plant diseases. To cope with this issue, we proposed an intelligent agriculture system based on a hybrid model of CNN and SVM, capable of detecting and classifying plant leaves disease early.

A deep neural network-based decision support system for intelligent geospatial data analysis in intelligent agriculture system

A deep neural network-based decision support system for intelligent geospatial data analysis in intelligent agriculture system

A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing

Abstract An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.

Intelligent Agricultural System Based on IoT and Machine Learning

Intelligent Agricultural System Based on IoT and Machine Learning

Design of Intelligent Control and Monitoring System for Agriculture Based on Renewable Energy and IoT

As the population rises, agriculture is becoming a big growth field worldwide. Improving production and farm efficiency without constant manual supervision to satisfy the rising demand for food is a major challenge in agriculture. Climate change is also a significant issue for the agriculture industry, aside from the growing population. The research aims to provide the Internet of Things (IoT) with an intelligent agriculture system. Intelligent farming that provides high precision crop management, helpful data collection and automatic farming techniques can be used. This work provides an intelligent framework to track ground moisture and temperature in agriculture. Without human interference, appropriate action is taken after analyzing sensed data dependent on these principles. Temperature & relative humidity are assessed and processed for further data analyses in the Thing Speak Database.

Developing an Intelligent Agricultural System Based on Long Short-Term Memory

Developing an Intelligent Agricultural System Based on Long Short-Term Memory

Framework Design IoT for Smart Agriculture

West Bandung Regency has a very abundant potential of natural resources, one of which is in agriculture. Agriculture is the use of resources made by humans to produce food and manage their environment. One of problem is the inability of farmers to supervise and take care of crops so that an intelligent agricultural system based on IOT is required. The intelligent agricultural system aims to predict and monitor the problems faced by farmers to increase the productivity of agricultural products. In this study, it proposed a design of intelligent agricultural framework by configuring several sensors that generated data from agricultural land and then the information is presented in the monitoring system for the use of the user to determine the decisions in conducting agricultural activities.

Intelligent Agriculture System with Crop Selection Using Internet of Things

This paper is about the prototype implementation of Raspberry pi based intelligent agriculture system using IOT. This system tries to implement irrigation from places where the physical examination of data is difficult or not needed. The main objective of this paper is detection of diseases at the early stage. We mainly focus on machine learning algorithm like KNN. This includes a series of steps from capturing the real time image of leaf to spotting the diseases. Irrigation in the India is primarily based on the rainwater, which is also the primary source of water. The soil type is instrumental in deciding in what way crops are to be provided with water by checking water level of soil. From the data established or gets from sensors and current condition of the soil , best suited crop is selected. Parameters such as temperature, humidity, and moisture and water pump status are shown on mobile app. Values of these parameters are changed according to best condition essential for the particular crop.

SMART AGRICULTURE BASED IOT AND MOBILE APPS

Agriculture is the main occupation in our country for many centuries. But now because of the migration of people from rural to urban areas there are barriers to agriculture. So to solve this problem we are using smart farming techniques using IOT. Smart farming is a developing concept, because the IOT Sensor is able to provide information about the agricultural sector and then act on user input. To develop this Intelligent Agricultural System, it uses the advantages of the latest technology such as Arduino, IOT and Wireless Sensor Networks. The paper aims to make use of the emerging technology namely IOT and the use of intelligent agricultural automation. Monitoring of environmental conditions is the main factor for increasing efficient crop yields. This feature includes the development of a system that can monitor temperature, humidity and is programmed via an android application.

Low financial cost with ant colony optimisation in intelligent agriculture

With the development of wireless sensor networks, and other information related high technologies, a lot of practical Internet of Things (IoT) applications have greatly increased the productivity. Currently, more and more capital is invested in IoT, especially intelligent agriculture as many countries begin to pay more attention to basic and intelligent agriculture. For a large intelligent agriculture system, it will cost a lot of time and energy for the mobile sink to collect all the data of the sensing system with the help of cluster head node. In this paper, we try to solve this issue by minimising the data collection path of the mobile sink, using the ant colony optimisation algorithm. We implement the algorithm in Python and conduct two experiments which show that we can get the best path of the given example and show how the efficiency changes when the numbers of ants and loops increase.

Analysis of intelligent agricultural system and control mode based on fuzzy control and sensor network

Analysis of intelligent agricultural system and control mode based on fuzzy control and sensor network

A Novel Recognition Algorithm Based on Optimal Wavelet Packet and Non-Negative Matrix Factorization for Extracting Pathologic Features of Plant Image

This paper presents a new recognition algorithm for plant pathology images based on the Non-negative Matrix Factorization, the proposed algorithm is combined with optimal wavelet packet basis to recognize patterns and conduct data encoding in the internet of things oriented intelligent agricultural system. The experimental results show that the performance of the proposed recognition algorithm is far better than those of the principal component analysis and linear discriminant analysis, and the recognition rate are improved, on average, about 14.65% and 11.18% higher than the rates of the above algorithms respectively. The presented algorithm is characterized by the fast speed, high calculation accuracy and easy hardware implementation.