Abstract
Internet of things solutions with machine learning capabilities is a hot research area in industries, including agriculture. They can be used for data analysis and further forecasting the big data and intelligent applications in farming. In traditional farming, the main obstacles are disease prediction, automatic irrigation, energy harvesting, and constant monitoring. Today, farmers' cultivation of their crops has changed by introducing automated harvesters, drones, autonomous tractors, sowing, and weeding. Smart farming with ML-enabled IoT systems can improve crop harvesting decisions. The main topic of this chapter is to provide an ML-enabled IoT solution for smart agriculture. The MIoT solution in agriculture allows farmers to use predictive analytics to help them make better harvesting decisions. Designing a MIoT system for smart agriculture can assist farmers in improving yields, planning more effective irrigation, and making harvest forecasts by monitoring essential data like humidity, air temperature, and soil quality via remote sensors.
TopIntroduction
The world's primary source of food is agriculture. Civilizations have depended heavily on it throughout history. The significant complications in traditional agriculture are unpredictable contamination of crops, inclement weather, ineffective pest management regulations, and food safety monitoring. These complications financially impact the agriculture sector, and there are more hazards to the public. Traditional farming techniques are obsolete, and the food supply chain is becoming increasingly complex. The farmer was always out in the field, keeping an eye on the land and the health of the crops (Kodali Ravi et al., 2016).
Motivation
Traditional farming practices (FAO 2015 & FAO 2017) are challenging due to climatic fluctuations and extremities, growing population, urbanization, loss of crops to various diseases, and difficulty detecting weeds. These challenges are motivated to apply a technology-based solution to increase the quality and quantity of the crops.
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Growing crops in populated cities and population hikes could favour increasing urbanization. Therefore, food is needed, and the limited land requires practical solutions to ensure food supply.
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The loss of crops to various diseases is a major issue in farming. Pests and disease control are significant concerns in agriculture. In traditional practice, spraying pesticides over cropping areas produces high environmental costs.
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Weeds are the most critical threats to crop detection. It is challenging to identify weeds and tell them apart from crops. Hence, weed detection is a significant problem in agriculture.
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Crop production can be impacted by extreme weather changes and climates, raising crop costs and degrading crop quality.
Farmers are turning to technologies to automate agricultural production (AaronTan, 2021), alleviating the need to toil on the land while watching their crops. They change how they cultivate crops using robotic harvesters, drones, self-driving tractors, sowing, and weeding. These operations maximize human labour while raising crop quality and yield.
Key Terms in this Chapter
Edge Computing: A networking focused on bringing computing as close to the source of data as possible to reduce latency and bandwidth use.
Autonomy: A technology that can function without being told what to do. Examples are driverless tractors, cultivators, sprayers, and harvesters.
Smart Farming: This is about using the new technologies that have arisen at the dawn of the Industrial Revolution 4.0 in agriculture and cattle production to increase production quantity and quality by making maximum use of resources and minimizing the environmental impact.
Precision Agriculture (PA): A farming management strategy based on observing, measuring, and responding to temporal and spatial variability to improve agricultural production sustainability. It is used in both crop and livestock production.
Urban Farming: (urban agriculture or gardening) Cultivating, processing, and distributing food in or around urban areas. It encompasses a complex and diverse mix of city food production activities, including fisheries and forestry.
Cloud Computing: Technology is on-demand internet access to computing resources such as applications, servers, data storage, development tools, networking capabilities, and more hosted at a remote Data Center managed by a services provider.
Vertical Farming: This is the practice of growing crops in vertically stacked layers. It often incorporates controlled-environment agriculture, which aims to optimize plant growth, and soilless farming techniques such as hydroponics, aquaponics, and aeroponics.
ML-enabled IoT (MIoT): A: system consisting of a sensor network that studies lighting and cloud patterns to predict the weather is successfully deployed in agricultural environments.
Geographic Information System (GIS): A computer system for capturing, storing, checking, and displaying data related to positions on Earth's surface.
AgTech: Any innovation (including hardware, software, business models, new technologies, and Apps) used across the value chain to improve efficiency, profitability, and sustainability.
Sensor: A device that measures or detects environmental changes to present data for decision-making. This data can deliver benefits through improved crop and livestock yields; reduced wastage and livestock mortality; automation of farm operations; and maintenance or cost savings. Sensors may measure soil moisture/nutrition, weather, and water storage levels.
Deep Learning (DL): Technology is a method in AI that teaches computers to process data in a way inspired by the human brain. DL models can recognize the complex picture, text, sounds, and other data patterns to produce accurate insights and predictions.
IoTAg (IoT-enabled Agriculture): Represents technology wherein agricultural planning and operations connect in previously impossible ways if not for advances in sensors, communications, data analytics, and other areas.
Agricultural Robots (Agribots): A robot designed for agriculture to automate tasks for farmers, boosting production efficiency and reducing the industry's reliance on manual labor.
Big Data: Technology provides farmers with granular data on rainfall patterns, water cycles, fertilizer requirements, and more. It enables them to make smart decisions (such as what crops to plant for better profitability and when to harvest), ultimately improving farm yields.
Blockchain: Technology is a decentralized, distributed, and public digital ledger used to record transactions across many computers so that the record cannot be altered retroactively without altering all subsequent blocks and the network consensus.
Data Mining technology: Sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.
Controlled Environment Agriculture (CEA): The production of plants and their products, such as vegetables and flowers, inside controlled environment structures such as greenhouses, vertical farms, and growth chambers.
Data Science: Technology is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.
Machine Learning (ML): Technology is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. ML algorithms use historical data as input to predict new output values.
Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. AI applications include expert systems, natural language processing, speech recognition, and machine vision.
Internet of Things (IoT): Technology refers to the collective network of connected devices and the technology that facilitates communication between devices and the cloud system.
Regenerative Agriculture (RA): An outcome-based food production system that nurtures and restores soil health, protects the climate and water resources and biodiversity, and enhances farms' productivity and profitability. It comprises various innovative technologies to combat climate change challenges, soil health, and protect the land's ecosystem.
Smart Greenhouse: Combines conventional agricultural systems and new IoT technologies for complete visibility and automation. It helps pinpoint inefficiencies and combat issues that have plagued farming operations to protect crops and maximize yields.
5G Technology: A 5 th generation mobile, built on the current 4G network but with increased connection speeds and shorter delays.