Computer Vision in Smart Agriculture and Crop Management E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 584

Veröffentlichungsjahr: 2024

Beschreibung

This book is essential for anyone interested in understanding how smart agriculture, utilizing information and technology such as computer vision and deep learning, can revolutionize agriculture productivity, resolve ongoing concerns, and enhance economic and general effectiveness in farming.

The need for a reliable food supply has driven the development of smart agriculture, which leverages technology to assist farmers, especially in remote areas. A key component is computer vision (CV) technology, which, combined with deep learning, can manage agricultural productivity and enhance automation systems for improved efficiency and cost-effectiveness. Automation in agriculture ensures benefits like reduced costs, high performance, and accuracy. Aerial imaging and high-throughput research enable effective crop monitoring and management. Computer vision and AI models aid in detecting plant health, impurities, and pests, supporting sustainable farming. This book explores using CV and AI to develop smart agriculture through deep learning, data mining, and intelligent applications.

Leseprobe

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Computer Vision in Smart Agriculture and Crop Management

Edited by

Rajesh Kumar Dhanaraj

Balamurugan Balusamy

Prithi Samuel

Malathy Sathyamoorthy

and

Ali Kashif Bashir

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-18629-7

Front cover images supplied by Adobe FireflyCover design by Russell Richardson

Preface

As editors, we feel privileged to have been asked to edit the 1st edition of Computer Vision in Smart Agriculture and Crop Management. The need for a sufficient food supply and societal demands has driven the evolution of smart agriculture. Smart agriculture leverages information and technology to assist farmers, particularly in remote locations. A key component of advancing agricultural automation systems is Computer Vision (CV) technology. By integrating computer vision with other intelligent systems, such as deep learning, it is possible to manage agricultural productivity using massive datasets, addressing persistent agricultural challenges and enhancing the economic, general, and operational efficiency of agricultural automation tools. Automation in agriculture offers significant benefits, including reduced costs, high performance, and increased accuracy, all contributing to sustained growth. Aerial imaging is commonly used during the growing season to monitor crops. High-throughput phenotyping research is expected to provide substantial insights into yield attributes on a large scale, supporting effective crop management decisions.

Machine learning and computer vision techniques help farmers distinguish between fertile soil, natural remedies, and pest control measures. These technologies can also assess the color, size, thickness, and surface texture of crops to detect impurities in agricultural yields and identify contaminated food products. Computer vision-based Artificial Intelligence (AI) models are highly effective for detecting and monitoring plant health. Furthermore, AI aids in developing more accurate seasonal forecasting techniques, which enhance production and operational efficiency. Innovations in computer vision, intelligent systems, and machine learning will eventually lead to the development of advanced remote sensing technologies for detecting and managing plants, weeds, diseases, and pests, thereby supporting sustainable agriculture and farming. The goal of this specialized area is to utilize computer vision and other emerging technologies to create smart agriculture and improve crop surveillance for societal benefit. This book focuses on using deep learning to extract relevant image content features from digital photogrammetry and applying data mining and machine learning techniques to organize these features into clusters for intelligent applications, ultimately achieving smart agriculture and sustainable farming.

Rajesh Kumar Dhanaraj

Pune, India

Balamurugan Balusamy

New Delhi, India

Prithi Samuel

Chennai, India

Malathy Sathyamoorthy

Coimbatore, India

Ali Kashif Bashir

United Kingdom

1Computer Vision-Based Innovations for Smart Agriculture and Crop Surveillance: Evolution, Trends, and Future Challenges

M. Nalini* and B. Yoga Bhuvaneswari

Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai, India

Abstract

The economic prosperity of a nation relies heavily on its agricultural industry. Due to population expansion, frequent climate change, and material restriction, it has become harder to supply the food needs of the present population. Precision agriculture, also frequently referred to as smart agriculture, is a cutting-edge method for addressing the challenges associated with agricultural sustainability. This cutting-edge technology is powered by a system based on computer vision (CV) and artificial intelligence (AI). These AI and CV techniques have made remarkable contributions to the agricultural industry in the areas of plant health detection and monitoring, planting, weeding, harvesting, and modern weather forecast analysis. So many use cases of smart farming have an influence on the whole food supply chain by offering insightful data on the entire agricultural process, easing operational decision-making in real-time, and improving farming methods by adding smart sensors and equipment to the field. The use of CV in agriculture has recently increased. Computer vision has enormous potential to improve the whole functioning of the agricultural industry, from lowering production costs through intelligent automation to increasing output.

Artificial intelligence includes the field of CV. Because of breakthrough technology, machines can now understand and perceive the visual environment in a manner that is comparable to that of humans. Computer vision techniques combined with image capturing from remote cameras provide agriculture-specific, non-contact, and scalable sensing solutions. The CV–AI models have made several contributions to the agricultural business in areas including harvesting, enhanced weather analysis, weeding, planting, and plant health detection and monitoring. This chapter covers the evolution, trends, and future challenges of smart farming using CV.

Keywords: Computer vision, smart farming, precision agriculture, artificial intelligence, intelligent farming, internet of things-based smart farming, machine learning, precision farming

1.1 Introduction

Precision agriculture is an agricultural management idea built on surveilling, measuring, and reacting to crop variances. By maximizing input returns while protecting resources, precision agriculture research tries to establish a decision-making system for farm management. This type of agriculture has benefited greatly from machine vision, which makes automated alternatives to chores that are often done by hand. Manual processes are usually laborious and vulnerable to mistakes. Accurate, precise, and effective solutions may be provided by machine vision to help agricultural activities. Additionally, machine learning (ML) techniques make it possible to analyze enormous amounts of data swiftly and reliably, opening the door for the adoption of machine vision applications in agriculture. For agricultural production to meet the issues of productivity, environmental impact, food safety, and sustainability, smart farming is crucial. Due to the continually growing world population, a significant increase in food production must be made while simultaneously maintaining high nutritional quality and availability everywhere and safeguarding the natural ecosystems by employing sustainable agricultural practices. Advanced computer vision (CV) algorithms and ML-based artificial intelligence (AI) can continually analyze the data gathered from multiple sources. Artificial intelligence, deep computing, and computational intelligence are all related technologies created for intelligent systems. A larger definition of AI is the development of intelligent computers that can mimic human thought and behavior. Simply expressed, the basic goal of AI is to develop computer systems that are intelligent enough to resemble human intellect. These AI systems need knowledge engineering to function properly.

Machine learning, on the other hand, is a branch of AI that enables computers to learn from collected data without explicit programming. Machine learning makes accurate predictions on new data by using sophisticated algorithms that repeatedly cycle over the massive data set. One dynamic and fascinating area of AI is CV, which replicates the intricate human visual system. The basic objective of CV is to enable computer systems to detect and locate objects in images and videos in a similar way to how people do. Two examples of cutting-edge methods that have considerably enhanced CV are deep learning and neural networks. Deep learning algorithms like the convolutional neural networks (CNNs) and their derivatives are frequently employed in in-plant phenology investigations. For classifying images, CNNs are frequently employed. The main building blocks of CNNs are convolutional layers, and to create a feature map, a window (filter) is utilized to scan the pictures and seek for certain features. Convolution, pooling, and fully connected layers are utilized to construct an entire CNN, which produces precise feature recognition and accurate picture categorization. With little information loss, pooling layers attempt to minimize the dimensionality of the feature map produced by the convolutional layers [12].

1.2 Artificial Intelligence in Agriculture

Artificial intelligence techniques are frequently employed in agricultural industries to solve a unique set of issues and improve production and operating methods. Agriculture can quickly adapt to using AI and ML when reading each sentence of agricultural products and agricultural techniques in the topic. Because it can comprehend, learn, and react to a one-of-a-kind (mostly based on learning) rising efficiency, of particular importance, cognitive computing is positioned as the catalyst in the next great agricultural revolution. With the use of AI, farmers may gain valuable information about their farm’s weather, temperature, water consumption, and soil monitoring, which will help them increase their earnings. By discovering which crops they can grow, creating high-quality hybrid seeds, and maximizing resource efficiency, AI technologies aid farmers. Both the quality of harvested products and the accuracy with which they are harvested may be improved with AI [11].

Precision agriculture uses AI technology to assist in the early diagnosis of diseased plants, insect infestations, and nutritional issues in agricultural areas. Artificial intelligence-powered sensors can quickly identify weeds and advise users to apply pesticides. Utilizing this method can save cultivation expenses since herbicides were made more widely known and sprayed throughout the entire field. Farms of any size across the world are using AI technology to operate more effectively and meet global food demand. With the use of AI, forecasting models may be more accurately and effectively learned. These models can predict weather accurately months in advance. The most efficient method for assisting small farmers is seasonal forecasting. Applications of AI are shown in Figure 1.1.

Artificial intelligence is positively transforming Indian agriculture in several ways. The value of AI applications in agriculture is predicted to increase from $852.2 million in 2019 to $238.38 billion in 2030, or a growth of about 25%. Access to markets, inputs, loans, and crop insurance are all improved by technology. A supply chain that is driven by demand may be built with the use of appropriate timing and precise data. Many AI models become inexpensive and accessible by utilizing sensors, phone photos, drones, agricultural weather data, and data on the condition of the soil. Various challenges, such as climate change, population increase, and job issues, are being addressed by AI, which is functioning as a catalyst for improved yield. Nowadays, relatively few individuals are interested in farming, which causes a labor shortage on many farms. In traditional farming, numerous laborers are needed to seed the crops and make the fields profitable. The use of AI farm bots is a way to deal with the labor scarcity. The AI bots are used in a variety of ways to supplement human labor. Bots can harvest crops more quickly than human laborers can, and they can readily spot and get rid of weeds, which can lower expenses [10].

Figure 1.1 Applications of AI in the agricultural sector.

Thanks to a multitude of technologies, such as wireless, wired, and cellular connections (5G or beyond) for the Internet of Things (IoT) and robotics (agricultural drones and agribots), smart agriculture (SA) has started to be deployed. Specialized hardware and expertise in wireless communication are required for AI in 5G and beyond. Deep learning (DL) and machine learning (ML) are frequently employed for communications that require a massive number of inputs, outputs, and beamforming to support 5G and beyond. The DL, reinforcement learning, and CNN are utilized in channel coding for effectively using the air interface for 5G communications. Long short-term memory-type algorithms are used to foresee resource requirements for 5G slicing, enabling the operator to deliver diverse services over a single infrastructure.

1.3 Evolution of Smart Agriculture

Around the turn of the 20th century, the first real-world uses of so-called smart agricultural technology have been concentrated on the potential of new automated data management methods to improve the efficiency of machines used in sowing or harvesting operations. The ability to connect the quantities of input/output material flow to an exact position of the equipment in the field at that moment resulted in a paradigm shift in how agricultural operations were performed (through suitable geo-spatial references). This entailed overcoming the restrictions of geographical variability to get a more thorough and logical understanding of production locations, while introducing new issues connected to the vast amount of data that needed to be managed. These characteristics are also evident in several more names that precision agriculture is still known by today, including location farming systems, target farming, and prescription farming. These terms frequently emphasize the necessity of viewing precision agriculture as a collection of technical possibilities designed to provide an accurate (i.e., geographical) understanding of field activities to produce a parallel timely management of the same. Yet, in this sense, the use of “precision” in precision farming (PS) seems to be limited to a straightforward “spatial” idea, which also raises questions about the necessity for “accuracy.” Without a doubt, the advancement of the positioning system technology has been a major factor in the spread of PF approaches [9].

Precision farming, according to an exhaustive description provided in 1997 by the U.S. Nuclear Regulatory Commission (NRC), is a “management technique that employs ICT in order to collect data gathered from different sources in view of their eventual application in decisions about production operations.” At first, this idea was restricted to systems of arable farming, primarily involving cereal crops. Later, it was expanded to integrate additional farming techniques, including orchards, vineyards, and animals. Notwithstanding the continued applicability of the initial concept, the primary purpose of PF has historically been to enable the introduction of sophisticated automated applications into the agricultural industry. Potential customers were left feeling very confused and frequently let down by their entirely unmet expectations. Several farmers and industry researchers have just lately begun to refer to the practice as “smart agriculture” (SA) [5––8]. This phrase is sometimes replaced by “Farm 4.0” (Agri-4.0) by analogy with Industry 4.0 (Industry-4.0), sometimes more to represent a vogue than to assert a new technical idea. The phrase “Industry 4.0” is warranted since the industrial sector has been experiencing what could be considered its fourth industrial revolution for the past 10 years. A comparable strategy can only be proven for agriculture if it shares a historical evolutionary vision like the one suggested in Table 1.1, and Figure 1.2 summarizes a visual comparison of the technical advancements that have taken place in the industrial sector [3].

We must acknowledge that a step has been skipped in the technical advancement of agriculture based on the examination of both Table 1.1, as well as Figure 1.1. It is the one that relates to the comparison to the third industrial phase (Industry 3.0, which roughly spanned the years 1970 to 2010), which was characterized by the introduction of information and communication technology (ICT) into manufacturing processes and the slow but steady spread of enterprise resource planning (ERP) systems in business management. In other words, there has not really been a revolution within agriculture pushed on by information technology (IT). On the contrary, during the Industry 3.0 phase of Table 1.1, electronic improvements were distributed, and during the Industry 4.0 phase, an additional integrated approach using the previously discussed PF logics was made [4].

Although the lack of expertise and IT tradition in the administration of agricultural operations continues to be a hindrance for the quick adoption of technological advancements in the industry, SA may also be considered from this perspective as an advanced step of Agri-4.0. Of course, there are some deviations, and they are all application domains that most closely reflect the logic of the business sectors regarding both their administrative and structural traits. Examples of these domains include vegetable nurseries, dairy farms, and the cutting-edge technology of the so-called vertical farming.

Table 1.1 Suggested potential classification of the developmental stages of agricultural technology advancements.

Agriculture

Period

Name

Description

Industry 0.0

Until 1920s

Traditional farming and ruralism

The utilization of manual labor and animal traction is common; in the latter, mechanized traction is gradually included.

Industry 1.0

1920s–1960s

Motorization

The introduction and adoption of advancements in the tractor industry, such as diesel-powered engines, pneumatic circuits, three-point linkages, and the “inventory” of tires; continuing manual effort in the fields; due to fascism’s autarkic agrarian policies, this era has continued in Italy; acceleration of change after World War II, thanks to the electrification of rural areas.

Industry 2.0

1960–1980

Mechanization

Improvements in tractors, such as an ongoing increase in their nominal strength and efficiency, the arrival of powerful operating machines in all agriculture-based manufacturing regions, the rapid substitute of manual laborers and the emigration of rural residents, and a sharp rise in primary yields due to developments in the chemical and genetics industries.

Industry 3.0

1980s–2000s

Humanism and electronics

Improvements to mechanized systems that pay more attention to the human–machine interface (HMI), the use of computerized control system on board tractor models and initial fixed point process automation approaches; the initial efforts to digitize farm management were never completely and widely strengthened, except in particular sectors like livestock husbandry; and finally, the use of electronic control systems on board vehicles and other portable machinery (especially in dairy farms).

Industry 4.0

After 2000

Precision agriculture, traceability, and cyber physical systems (smart farming)

Combination of gadgets and automation in all agricultural regions, with a focus on automating mobile-point operations (site-specific control); adoption of sensors for monitoring purposes and on-board tractor positioning system; protocols for communication between the device (CAN bus, ISOBUS, wireless networks, Wi-Fi, Bluetooth, etc.); encounters with computerization and the use of integrated information systems, particularly in large farms; proprietary farm information system proposals from major farm machinery makers; process connection and machine-to-machine connections; IoT, cloud, and fog computing; and processes and products certification geared toward traceability systems.

Figure 1.2 Evolution of precision or smart farming.

1.4 AI Technology Trends in Computer Vision

There are three fundamental steps in CV: (1) acquiring the image/video using a camera, (2) processing the acquired image, and (3) understanding the processed image.

1.5 Benefits of Artificial Intelligence in Agriculture

Artificial intelligence approaches are widely utilized to solve a unique set of problems and to improve the production and operating processes in the industries of agriculture, food, and bio-device engineering. The use of DL, ML, and AI, as well as the production of agricultural goods, is accelerating in the agricultural sector. The most revolutionary generation in agricultural services will likely be AI, which can perceive, learn, and react to different situations (totally based on learning) to boost efficiency [2]. The efficiency of current procedures has been considerably increased by cloud computing, big data, and the IoT. The IoT is expected to transform into the “Internet of Action” by the 2030s, with sensors and devices equipped with built-in AI and data analytics capabilities that enable self-optimization and autonomous activity initiation. This is how it could be beneficial:

1.5.1 Improving the Whole Supply Chain

The use of AI technology enhances the whole food supply chain by helping with the production of healthy crops, management of pests, soil surveillance, environment growth, data organization, manual work reduction, and enhancement of several agricultural tasks. The opportunity for AI in Indian agriculture is huge due to the large amount of data that Indian farmers can supply to develop AI solutions for the nation.

1.5.2 Agricultural Robotics

For farmers, a primary issue is weed control, which is difficult to accomplish because pesticide resistance is on the rise. Using agricultural robots, farmers may more effectively develop weed-control strategies for their crops. After sending visual data to a computer or a robot’s system, the robot sprays on the weeds in cotton plantations to stop or reduce the growth of weeds.

Spraying accurately can stop herbicide resistance.

1.5.3 Policy, Governance and Market Access

A broad variety of big data is becoming exponentially more accessible because of farmers’ extensive adoption of digital technology, which can help with improved policymaking and the transformation of the agricultural industry. The ICT-powered innovative media platforms fill the gap between agricultural extension agents and researchers on the one end and farmers on the other. It is a less expensive way to improve smallholders’ familiarity with the most recent agricultural techniques and markets. Market information services facilitate farmers’ access to neighboring marketplaces and increase their knowledge of current consumer wants by transmitting information from dealers with affordable mobile phones, Internet, and other information-dissemination services, as well as expanding rural farmers’ access and understanding of climate-smart technologies.

1.5.4 Early Warning System

Governments and communities can get up-to-date information on disaster management and prevention through early warning systems. By giving individuals prompt advice on risk reduction techniques, they also facilitate more effective communication and increase the effectiveness of an emergency response.

1.5.5 Food Safety and Traceability

Mobile phones, software programs, RFID tags, data entry websites, and GPS-enabled sensors are just a few examples of the simple and complex technologies that manufacturers use to collect and analyze trustworthy data while still abiding by global traceability rules.

1.5.6 Financial Inclusion and Risk Management

It facilitates small-scale farmers’ and rural farmers’ access to financial services, helps them locate cost-effective insurance plans and risk management instruments, and informs them about the financial services that are accessible to them.

1.5.7 Capacity Building and Empowerment

It serves as a crucial educational instrument for the development of regional community. By expanding the reach of women, young people, and also other beneficiaries, they open the door to more recent business prospects that can enhance livelihood and earnings.

1.5.8 Growth Driven by IoT

Every day, enormous amounts of unstructured and independent information are produced. In agriculture, they can be about soil reports, fresh research, rainfall, insect infestation, pictures taken by drones and cameras, among others. Cognitive systems for the IoT can detect all these and offer reliable insights to boost crop output. Artificial intelligence may be used to develop intelligent systems that can be integrated into equipment that can work faster and more accurately than people.

1.5.9 Image-Dependent Insight Generation

An important area of farming that is currently most frequently discussed is PF. Drones generally help with intense topic evaluation, crop monitoring, field scanning, and other tasks. Drone data, IoT, and computer-generated vision may all be used to ensure quick and more efficient production with the help of farmers. Real-time signals can be generated by feeds from drone photo records to increase PF. Disease diagnosis, crop readiness identification, and field management are among the areas where computer-generated vision may be used.

1.5.10 Identification of Optimal Mix for Agronomic Products

Cognitive solutions give farmers recommendations about the many ways healthy crop production and yield can be achieved with hybrid seeds and plants based on a few elements like soil quality, climatic prediction, a particular epidemic, and a certain kind of seed. Suggested actions can also be tailored depending on the needs of the farm, the environment, and information about successful agricultural practices from the past. Farmers will be able to make an informed choice by taking into account outside factors including market trends, costs, and client preferences [1].

1.5.11 Monitoring of Crops and Soil Health

Building agricultural metrics across hundreds of acres requires remote sensing techniques along with hyperspectral imagery and 3D laser scanning. It may signal a gradual shift in terms of how farmers manage farms from both a labor and a time standpoint. In case of abnormalities, this generation may be utilized to exhibit vegetation together with their entire lifespan. One of the largest areas of agriculture that continues to provide drone-technology based remedies in cooperation with AI and web imaginative and prescient technology is crop monitoring and health evaluation. Drones equipped with high-precision cameras gather precise pictures that may be sent through a CNN to locate areas where weeds are present, which plants need water, and the amount or degree of stress some plants in the mid-bloom stage are. By analyzing plant life in both near-infrared and RGB light, drone technology can easily provide multispectral photos of ill and affected plants. This makes it much simpler to identify which plants in a specific region are affected so that remedies may be used immediately. Hyperspectral images and 3D scanning techniques are used in multispectral images to create a spatial data device that may be used to delineate acres of land.

1.5.12 Automation Techniques in Irrigation and Enabling Farmers

Irrigation is an agricultural practice that is human labor intensive. Machines knowledgeable on past climatic trends may improve soil, choose the right plants to grow, automate watering, and increase overall productivity. Automation in irrigation can help farmers better regulate use of water because irrigation consumes close to 70% of global freshwater.

1.5.13 Drones: The New Buzz in AI-Driven Agriculture

The production of agricultural drones has increased over the past few years, and farmers are now more aware of the usage of drones in agriculture. Drone applications in agriculture range from crop dusting and spraying to surveying and mapping. When dealing with bad weather, productivity enhancements, precision agriculture, and yield management, drone-based solutions in agriculture are very important. Before a crop cycle, a 3D field map with precise geography, drainage, soil viability, and irrigation might be provided by the drone. Drone technology may also be used for nitrogen level regulation. The earth is sprayed from above with pods containing seeds and plant vitamins, providing plants with essential dietary nutrients. In addition, depending on the topography, drones may be programmed to springle by adjusting their distance from the ground. Crop health monitoring at mid-season is the most common use of drones in farming with the use of near-infrared region (NIR) or normalized difference vegetation index (NDVI) sensors to scan crop growth from a height of roughly 100 meters. Traditionally, this duty was carried out by humans who entered the fields with a notebook. A modern drone enables the capture of data that cannot be seen by the normal human eyes, as well as cover larger surface areas in a shorter amount of time (like the NDVI or near-infrared). Also, it greatly reduces the possibility of human mistakes in traditional inventory work, while it is still advised to physically verify a potential problem location after seeing the images [14].

1.6 Precision Farming

“Right place, right time, right product” is a catchphrase used to describe PF. The repetitious and labor-intensive portion of farming is replaced with a more accurate and controlled method. Additionally, it provides guidance on the best times for planting and harvesting, crop rotation, water and fertilizer management, insect attacks, and more. The goals of precision agriculture are strategic selection of crops and markets: This is necessary for profit-abilityand estimating ROI using cost and margin data. Efficiency: A precise algorithm enables better, quicker, and more affordable agricultural options. Overall accuracy and resource efficiency are enhanced by doing this. Sustainability: Better social, environmental, and economic performance guarantees that all performance metrics gradually advance each season.

Precision farming can significantly improve agricultural practices to boost productivity and lessen the environmental effect of pesticide waste. It is likely that adopting technology with the necessary capabilities to offer fully supported PF will be an evolutionary process, with farms steadily enhancing their IT capabilities. Majority of the necessary components for a full PF system have had significant advances, enough for the system to be used in full by farmers in the near future. The software that assists farmers in choosing appropriate treatments and treatment doses to be spatially administered is the area that is expected to require ongoing improvement for the foreseeable future. As more businesses enter the market, the cost of IT components is anticipated to decrease. This new strategy is not only rational, but it is also technically possible, as well as being environmentally and economically justified [15].

1.7 Future Challenges

One of the crucial industries that contributes to a nation’s security and stability is agriculture, which is also most impacted by climatic and environmental changes. Throughout their growth and development, plants are subjected to a variety of biotic and abiotic stressors. Some of the important challenges are shown in Figure 1.3.

Issue with a single standard solution: To increase agricultural productivity, several technologies have been developed. Due to the agriculture industry’s continually changing environment, there are still no reliable predictions. The performance of suggested models is negatively influenced by the elements that have an impact on prediction accuracy, including soil quality, downpours, weather, and pests. As a result, the models’ performance in one field differs from their performance in the other, making it impossible to reproduce efficiency.

Figure 1.3 Future challenges in agriculture.

A shortage of interactions among scientists and farmers: The breakthrough technical advancements that could facilitate decision-making are not yet adopted by farmers due to their lack of access to such advancements. Some researchers are also not aware of any production-related challenges that developing technology may present to farmers. The study found that the best method to close this gap is to bring together researchers, agricultural experts, and farmers in a single group.

Data security and privacy: As smart agricultural technology develops, one of the main concerns among farmers is the issue of data privacy. Huge and varied data from numerous farmlands are needed to create an effective ML model. Yet, it is challenging to keep track of agricultural data. Transfer learning is a novel ML method that was created to overcome issues with data privacy. By allowing several data providers to simultaneously train and utilize a single model, it is important to protect the privacy of local training data. Ubiquitous ML allows training across dispersed machines utilizing local data samples without information transmission. Federated ML techniques may be used in the future to identify crops using satellite data [13].

Large public image data collections are scarce: Large public picture data sets are hard to come by, which makes creating expert systems and cutting-edge CV for smart farming a huge task. Nevertheless, several attempts have been conducted during the past 5 years to produce new collection of data for the efficient development and evaluation of models based on DL. The main online benchmark now is the plant village data set. Furthermore, gathering data from actual farmlands is still a costly, time-consuming, and error-prone process that calls for subject-matter specialists. There are now just a few data sets that deal with various crops in various geographical locations and seasons. Consequently, significant work is still needed to fill this void and increase the data sets for SA.

Recognition of diseases in various crop parts: The illness in the upper section of the plant leaf has received most of the attention in studies up to this time. In terms of diagnosing other plant diseases, other body parts including flowers, fruit, and stems have not had much attention. Only a few attempts have been made to identify illnesses in various plant components. Therefore, technologies must undergo the laborious process of picture annotation and data set labeling for various plant sections. Finally, a technology that can effectively identify agricultural diseases needs to be developed.

New learning model development: Several models constructed using ML have been reported in the literature and have demonstrated promising outcomes. Numerous DL models are based on Alex net, Resnet, Visual Geometry Group, Inspectionv3, and GoogLENet’s five CNN designs. The accuracy and speed of calculation are frequently improved using CNN architecture. The problem of determining the number of layers and the type of layers required for an optimal solution remains unsolved. To adapt the improved CNN design, a hit and trial method is used. Additionally, these models may fall victim to the overfitting problem since they are unable to handle a variety of data sets. New learning models must be created to address the issue.

Hardware and software IoT shortcomings: In the previous two decades, the usage of smart devices in the agriculture sector has significantly increased awareness on the IoT. However, there are three significant obstacles to real-time implementation of IoT-based systems. First, the lack of expert sensors is the main impediment to the development of sophisticated farms, especially in plant phenotyping. Second, communication shortages in networks, particularly among rural locations, lead to erratic data transfer, which could have an impact on the overall system. Third, ongoing research and development are yet to provide a dependable and strong system that transfers data across wired and wireless media, especially in rural areas. Furthermore, because of the diverse nature of agricultural applications, a single approach is not feasible due to the environment’s variable behavior. Therefore, a mechanism for evaluating how well a communication medium performs in various contexts needs to be developed.

Functionality and interoperability challenges: Diverse business and technical models cannot be supported by a single organizational structure. In addition, during data transfer, several security risks, including wiretapping, replay attacks, and data tampering, may be tried, endangering security. Thus, it is necessary to guarantee data integrity, confidentiality, and authenticity during data collection and transfer. To prevent unwanted access to data, various route authentication policies should be used. Another issue that could impede the expansion of a smart farming business is capital investment in agriculture. Additionally, it is difficult to get affordable IoT equipment for small-scale farmers.

Managing the big computing data: Several IoT gadgets generate a large collection of data. Intelligent infrastructure is needed to manage, process, and archive information for the automated execution of various activities. Infrastructure as a service solution is also quickly gaining favor because of they are inexpensive. There are many IoT systems available, including Salesforce IoT Cloud, Cisco IoT Cloud Connect, Amazon Web Services, and ThingWorx. These platforms can aid farmers in the future by making economic transactions easier. The challenges of technology still include data collection, hardware and software compatibility, decentralization, remote places with poor connectivity, and an inadequate number of techniques for making decisions.

Technology hurdles: To increase the use of agricultural robots, a number of technological obstacles related to robotics and autonomous systems must be overcome. The first difficulty is finding the hardware necessary to create agricultural robots. Second, for communication between humans and robots, there must be reliable wireless connectivity. Wireless devices can be used to track the whereabouts of autonomous systems, particularly when there is no visual contact. Third, creating software for agriculture robots is difficult. This entails creating software that can effectively manage various kinds of sensor, steadiness on uneven ground, and the capacity to dodge obstacles. Fourth, effective robot operation requires robot supervision to assure effective human–robot interaction.

Organizational challenges: For robotic and automated technology to be used in SA, farmer engagement is essential. Most farmers struggle to use technology correctly, which keeps them in the dark about important issues. To overcome this, there is a need for knowledgeable individuals who can help farmers use robots. Additionally, a government policy framework will help in a large-scale distribution of robots for agriculture to small farmers.

Financial difficulty: The cost of automated agriculture for autonomous and robotic systems is one of the major problems. Now, pushing robotics is seen as a way to increase agricultural productivity. An infrastructure made up of hardware and software is required to achieve this. However, the cost of the infrastructure’s technological installation is prohibitive. Additionally, many farmers might not be able to afford it. This could make using agricultural robots unfeasible in the first place. Therefore, to meet the impending issues of increasing food consumption, rising wages, a declining workforce, and environmental concerns, considerable long-term evolutionary adaptations are needed. The abilities needed to manage a farm may also change because of these modifications.

Managing mobility and electricity challenges: One of the most crucial elements in the realm of smart farming is the issues associated with mobility and power management. For real-time analysis, smart farms require an elevated degree of mobility assistance because they frequently gather fresh data from the agricultural field. If the farms are solely connected via the cloud, certain features are unavailable. To collect data from farms, fog computing has the capacity to do so. Furthermore, to do this operation, a consistent fast speed is needed. Better power management is needed to address this issue. Since a variety of devices, including mobile phones, are used to collect data, sensors and actuators are employed, and delivering data to an edge node demands a lot of power, which is deficient in some circumstances. It is possible to increase the lifespan of sensor nodes by using sources of clean energy like solar and wind power.

High hardware costs and trouble connecting to the Internet: Since edge devices transmitted real time data constantly to cloud servers. Smart infrastructure is required when handling, analyzing, and storing data to automate numerous activities. This task necessitates the use of numerous hardware components, each of which consumes more energy. Therefore, to reduce the hardware cost issues in SA, effective cost management is necessary. Additionally, bad Internet connectivity is another typical issue, particularly in rural locations. An Internet connection with a fast speed is necessary to handle the massive amount of data. However, when connected to the cloud, inadequate Internet connectivity in actual farmlands suffers a number of difficulties, including computational delays, delayed replies, data loss, and slow data uploading speeds.

Environmental issues in cyber-physical systems (CPS): As IoT devices are used in agricultural fields, CPS have attracted a lot of interest. However, there are a number of significant obstacles to real-time CPS adoption in smart agriculture for a variety of reasons. The first is the lack of fresh drinkable water, which is necessary to boost agricultural productivity. Second, dwindling agricultural land due to growing urbanization. The potential for soil erosion, which has a negative impact on productivity, is ranked third. To address these difficulties, a sustainable SA system with highly adaptive technologies is required. Furthermore, because the environment behaves differently depending on the application, a single solution cannot be used for all agricultural applications. Despite this, the advancement of modern technology makes use of CPS, one of the finest areas for application. However, further research is necessary to determine whether CPS will be successful in smart farming given the issues raised above.

Challenges in real time implementation: Using CPS to build SA applications in the real world is fraught with difficulty. There is not a single structure that can satisfy the requirements of every gadget. The various behaviors of weather conditions present a number of functional issues. Long-term preparation of animals and crops in accordance with consumer expectations is also necessary, in addition to sophisticated techniques to assess the condition of the soil and weather forecasts. Additionally, the sensors that monitor temperature, humidity, and the quantity of nutrients in the soil are being placed in actual fields to perform a constant quality check. Another difficulty is developing a productive management system for intelligent farming that combines real-time monitoring and a cooperative agent-oriented approach. The cost of implementation, however, impedes the development of the smart agricultural sector.

Challenges in design decisions: Design decisions have a variety of effects on present and forthcoming block chains, including OriginTrail, AgriLedger, FarmShare, and AgriDigital. For instance, authorization is required (i.e., partners are trusted) for an organizational structure that is free, and someone can join or leave. Depending on how the current permission-free block chains are set up, transactions may take anywhere from a few minutes to an hour to complete before everyone updates their financial information. Because of these architectural choices, block chain systems are rigid. Therefore, under certain situations, some blockchain-based solutions might not be as useful as traditional and centralized ones.

Usefulness of smart surveillance systems: The agricultural industry has placed a lot of pressure on technological advancements in smart farming. One of the biggest problems in the agricultural area is the smart monitoring of crop health on large fields. Swarm intelligence systems are necessary to make agricultural systems intelligent. The monitoring of several agricultural applications, such as water and soil, irrigation systems, surveillance of assets, greenhouse expansion, remote controlling, and diagnostics, are covered by swarm intelligence. To be able to monitor agricultural diseases, agricultural robots can be used in swarms. For farmers to take prompt action, it is be possible to develop a system for delivering real time information.

Agriculture autonomous system: Over the past 10 years, several autonomous systems have been created to boost production and dependability, and they have partially replaced humans. Agricultural operations are becoming more intelligent with the aid of modern robotics and autonomous systems. These tools are used in a variety of SA production methods, including plant factories, 3D printing of food, aerial pesticide application, farming that is biodiverse and self-sufficient, and automated husbandry. However, there are still a lot of issues that need to be resolved, such as exact identification, autonomous route navigation, and operational efficacy. Some cognitive systems are still required for smart farming, such as exact task distribution, item translation, and human–robot interaction.

Generating fresh data sets: The main challenge is the creation of new data sets because massive data sets are necessary to train models of DL or ML. Generative adversarial networks (GANs) have been used to combat this in several fields, and they are currently being used in AgroVision. Data wrapping and GANs are two recent examples of data augmentation techniques used to train models rather than creating synthetic images that are representative of actual farming conditions on a big scale.

Model choice: Difficulties in image improvement, crop identification, early-stage virus identification, plant-disease identification, and salt tolerances are some of those addressed by using GANs in SA. To examine diverse applications, a variety of customizable GAN models have recently been introduced. OneGAN, DoubleGAN, CGAN, DICNN and ARGAN, Auxiliary Classifier—GAN, Leaf GAN, Style GAN, Cy-cle Consistent-GAN, Wasserstein GAN-GP, and Enhanced Super-Resolution GAN are some of the GAN models that have been presented to the agriculture industry. For scientists and academics, choosing the best GAN model for a specific crop is a difficult issue.

1.8 Conclusion

The establishment of a strong and resilient agricultural system is essential for the future of agriculture. Currently, SA is structured. Likewise, the development of new automation technologies in agriculture may be hampered by a lack of awareness about technical breakthroughs. As a result,