An AIoT-Based Automated Farming Irrigation System for Farmers in Limpopo Province

Limpopo, one of South Africa's nine provinces, is mostly rural, where agriculture serves as the primary occupation for around 89 percent of the total population. Agriculture relies on water, making it its most valuable asset. Through irrigation, water is supplied to crops for growth, frost control, and crop cooling. Irrigation can occur naturally, as with precipitation, or artificially, as with sprinklers. However, artificial irrigation is wasteful as it is regulated and monitored through human intervention, leading to water scarcity which is one of the obstacles that threatens the agricultural sector in the province of Limpopo. A machine learning precipitation prediction algorithm optimizes water usage. The paper also describes a system with multiple sensors that detect soil parameters, and automatically irrigate land based on soil moisture by switching the motor on/off. The objective of this paper is to develop an automated farming irrigation system that is both efficient and effective, with the intention of contributing to the resolution of the water crisis in the province of Limpopo. The proposed solution ought to be capable of decreasing labour hours, generating cost savings, ensuring consistent and efficient water usage, and gathering informed data to inform future research. Thus, farmers will have greater access to information regarding when to irrigate, how much water to use, weather alerts, and recommendations. In acquiring these, the ARIMA model was applied alongside DSRM for implementing the mobile application. The results obtained indicate that the use of AI and IoT (AIoT) in agriculture can improve operational efficiency with reduced human intervention as there is real-time data acquisition with real-time processing and predictions

Identifying Advantages and Disadvantages of Variable Rate Irrigation – An Updated Review

Variable rate irrigation (VRI) sprinklers on mechanical move irrigation systems (center pivot or lateral move) have been commercially available since 2004. Although the number of VRI, zone or individual sprinkler, systems adopted to date is lower than expected there is a continued interest to harness this technology, especially when climate variability, regulatory nutrient management, water conservation policies, and declining water for agriculture compound the challenges involved for irrigated crop production. This article reviews the potential advantages and potential disadvantages of VRI technology for moving sprinklers, provides updated examples on such aspects, suggests a protocol for designing and implementing VRI technology and reports on the recent advancements. The advantages of VRI technology are demonstrated in the areas of agronomic improvement, greater economic returns, environmental protection and risk management, while the main drawbacks to VRI technology include the complexity to successfully implement the technology and the lack of evidence that it assures better performance in net profit or water savings. Although advances have been made in VRI technologies, its penetration into the market will continue to depend on tangible and perceived benefits by producers

A systematic review of fourth industrial revolution technologies in smart irrigation: Constraints, opportunities, and future prospects for sub-Saharan Africa

The adoption of Fourth Industrial Revolution (4IR) technologies has revolutionized agricultural practices worldwide. However, their application in the context of sub-Saharan Africa remains a critical challenge. This study presents a systematic review that investigates the potential of 4IR technologies in smart irrigation. Sub- Saharan Africa faces multiple agricultural challenges, exacerbated by climate change, water scarcity, and inefficient irrigation practices. The need for sustainable, water-efficient, and data-driven irrigation systems is urgent to ensure food security and economic development in the region. This study addresses a crucial knowledge gap by assessing the constraints, opportunities, and prospects of implementing 4IR technologies for smart irrigation in sub-Saharan Africa. A systematic review methodology was employed, utilizing reputable databases including Web of Science, Google Scholar, Science Direct, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and PubMed. A comprehensive search strategy was designed to identify peer-reviewed articles, conference papers, and reports related to the application of Internet of Things, Artificial Intelligence, Unmanned Aerial Vehicles, Big Data, and Blockchain technology in smart irrigation. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was used to arrive at 95 articles included in this research. The review reveals promising trends in the integration of 4IR technologies for smart irrigation. 4IR technologies including Artificial Intelligence, Internet of Things, Big Data technology, Blockchain and drones are widely used in smart irrigation. These technologies have facilitated real time monitoring of soil moisture and weather conditions, precision irrigation scheduling, water allocation optimization, rapid data collection, insights in real time crop water use and transparency and trust in agricultural water management among others. While the potential of 4IR technologies is evident, challenges including limited infrastructure, access to technology, and technical expertise pose significant barriers for the adoption in sub-Saharan Africa. Additionally, the high initial costs associated with these technologies can impede widespread adoption. The study highlights the potential to leverage existing mobile phone penetration for IoT data collection, collaborative partnerships, and innovative financing models to overcome financial constraints. Capacity-building initiatives and knowledge transfer can empower local communities to embrace these technologies. The future of smart irrigation in sub-Saharan Africa relies on policy support, investments, and technology diffusion strategies. It is imperative to create an enabling environment that fosters innovation and addresses the unique needs of the region. Collaboration between governments, academia, industry, and international organizations can catalyze the transformative impact of 4IR technologies, ultimately enhancing irrigation sustainability and food security

Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world\u27s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system

The Design and Performance Evaluation of a Wireless Sensor Network Based Irrigation System on Different Soil Types

In the Nigerian economy, agriculture plays a very important role, and most of its people depend on it for their livelihood. Agricultural practices in the country are still mainly based on conventional, traditional methods of farming which usually results in wastage of water resources and low production of crops to meet the country\u27s demand. There is a need to transform farming from the traditional way to a more efficient method with optimum water utilization. Irrigation is an assistive measure to salvage the problem of inadequate water for dry season farming. Irrigation consumes a lot of water, time and must be done on a timely basis. The automated irrigation system helps to curb the problem of overwatering and under watering of the land. This research proposed an Arduino-based smart irrigation system using a wireless sensor network to overcome the problem of overwatering, underwatering, and efficient time utilization in farming. The system is implemented using Arduino IDE, Proteus Simulation Tools, and Blynk Platform. The effect of the four-mobile network: MTN, GLO, Airtel and 9mobile on response time for Gidan- Kwano area was evaluated. Testing carried out on the system resulted in a response time of 0.75 seconds for Glo 2G network and 0.45 seconds for Glo 4G network. Less than 1sec in the worst-case scenario. This makes the system effective in terms of time response, thereby eradicate the waste of time that manual system operation poised to irrigation scheduling. Also, the appropriate soil moisture content is maintained, whether it rains or not. This reduces excesses and ensures healthy plant growth, increasing agricultural productivity, and cultivating crops are made possible throughout the year. The system also will help in driving agricultural innovation through the use of IoT. &nbsp

IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

A novel approach to analysis and prediction is provided by the internet of things-based time monitoring and prediction system using wireless sensor networks (WSN) and machine learning techniques (ML). To give accurate meteorological data in real time, the integrated system uses IoT, WSN, and ML. Making informed decisions requires these insights. Includes strategically positioned infrared points that are used to gather meteorological information, such as temperature, humidity, pressure, and wind speed, among other things.The machine's automatic data processing methods are then used in a central processing unit to collect and analyse the data. By seeing patterns and drawing diagrams utilising previously collected data, ML models are able to comprehend intricate temporal dynamics. An important development in this system is its predictive capabilities. Artificial intelligence has the processing power to precisely forecast short-term weather patterns, enabling the rapid transmission of warnings for extreme localised events and the reduction of potential dangers.The combination of historical data, real-time sensor inputs, and automated analysis produces the predictive potential. The "Internet of Things" architecture used to develop this system makes it simpler to gather meteorological data. A number of industries, including as agriculture, transportation, emergency management, and event planning, are encouraged to make data-based decisions since users can quickly obtain current meteorological conditions and forecasts through user-friendly web interfaces or mobile applications

Fuzzy logic system for drug storage based on the internet of things: a survey

The rapid development of internet of things (IoT) technology over the course of recent history has made it possible to connect a large number of smart things and sensors, as well as to establish an environment in which data can be seamlessly exchanged between them. This has led to an increase in the demand for data analysis and storage platforms such as cloud computing and fog computing. One of the application areas for the internet of things that has garnered a lot of interest from the business world, academic institutions, and the government is healthcare. The IoT and fuzzy logic are being used in the medical business to improve patient safety, the overall quality of care, and the overall efficiency of medical operations. The most important healthcare studies that are pertinent to pharmacies have been used as the basis for this research. The purpose of this research is to investigate recent advancements in medical modules, remotes, and detector patterns, as well as current innovations in IoT and fuzzy logic-based health care, and current policies from around the world, with the intention of determining how well they support the long-term growth of IoT and fuzzy logic in healthcare

INTELIGÊNCIA ARTIFICIAL E SUAS FERRAMENTAS NO CONTROLE DE PRAGAS PARA PRODUÇÃO AGRÍCOLA: UMA REVISÃO

Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.La inteligencia artificial y sus herramientas se están utilizando ampliamente en todo el mundo. Su uso en la agricultura está siendo ampliamente estudiado y ampliado, desde la precosecha hasta la poscosecha. El aumento de la población mundial ha desencadenado la necesidad de incrementar la producción de alimentos. Esta demanda desencadenó una búsqueda de soluciones que promuevan una mayor producción y calidad de los alimentos. Una forma de lograr este objetivo es el control de plagas. La inteligencia artificial y sus herramientas han demostrado ser una solución cada vez mayor para controlar y combatir las plagas. Esta investigación se centra en revisar y demostrar avances en el combate y control de plagas, utilizando herramientas e imágenes de inteligencia artificial. Se destacan actividades como clasificación de plagas, identificación de insectos, uso y captura de imágenes por UAV, además del uso de aprendizaje profundo y red neuronal convolucional. El estudio presenta el uso actual de la inteligencia artificial, el aprendizaje automático y el aprendizaje profundo, identificando las herramientas en uso y las soluciones propuestas o desarrolladas para combatir y controlar las plagas. Esta investigación sirve como base para abordar los desafíos futuros relacionados con el uso de la inteligencia artificial y sus herramientas en la identificación de plagas en imágenes reales, brindando conocimientos a los investigadores interesados ​​en desarrollar estudios sobre el uso del aprendizaje profundo en la agricultura.Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.A inteligência artificial e suas ferramentas estão sendo amplamente utilizadas em todo o mundo. O seu uso na agricultura está sendo amplamente estudado e expandido, abrangendo desde a pré-safra até o pós-safra. O aumento da população mundial tem desencadeado a necessidade de aumentar a produção de alimentos.  Essa demanda desencadeou uma busca por soluções que promovam o aumento da produção e qualidade dos alimentos. Uma forma de alcançar esse objetivo é o controle das pragas. A inteligência artificial e suas ferramentas têm demostrado ser uma solução em crescimento e ascensão no controle e combate às pragas.  Esta pesquisa concentra-se em revisar e demostrar os avanços no combate e controle de pragas, utilizando ferramentas de inteligência artificial e imagens. Destacam-se atividades como classificação de pragas, identificação de insetos, uso e captura de imagens por Unmanned Aerial Vehicle, além da utilização deep learning e convolutional neural network. O estudo apresenta a atual utilização da inteligência artificial, machine learning e deep learning, identificando as ferramentas em uso e as soluções propostas ou desenvolvidas para o combate e controle de pragas. Esta pesquisa serve como base para abordar futuros desafios referentes ao uso de inteligência artificial e suas ferramentas na identificação de pragas em imagens reais, fornecendo insights para pesquisadores interessados em desenvolver estudos sobre o uso de deep learning na agricultura

Water-COLOR: Water-COnservation using a Learning-based Optimized Recommender

Efficient water use, particularly in the realm of irrigation, has emerged as a critical concern in regions suffering from persistent drought, such as California and Florida. With the advent of smart irrigation controllers encouraged by environmental policies, a new paradigm of water management is gaining traction. Among these, the Rachio smart controller has garnered significant attention. However, without direct feedback or actual water usage data, optimizing these irrigation systems for enhanced efficiency remains challenging. This paper introduces Water-COLOR, a novel recommendation system integrated within the Rachio smart controller's framework to address this challenge. The system leverages similar landscape profiles to suggest irrigation schedules that are both water-efficient and user-preferable. By analyzing manual user interactions with the controller, Water-COLOR infers user satisfaction, which, along with estimated water usage, informs the adaptation of irrigation plans. The system eschews the need for additional sensors, thereby reducing infrastructure requirements. Our evaluation demonstrates consistent performance across diverse climatic regions and indicates that the system's recommendations could significantly contribute to water conservation efforts. The results not only showcase the potential of Water-COLOR to enhance the efficiency of existing smart irrigation systems but also open avenues for deploying real-time, data-driven environmental solutions