A Survey on Smart Agriculture:Development Modes,Technologies,and Security and Privacy Challenges

With the deep combination of both modern information technology and traditional agriculture,the era of agriculture 4.0,which takes the form of smart agriculture,has come.Smart agriculture provides solutions for agricultural intelligence and automation.However,information security issues cannot be ignored with the development of agriculture brought by modern information technology.In this paper,three typical development modes of smart agriculture(precision agriculture,facility agriculture,and order agriculture)are presented.Then,7 key technologies and 11 key applications are derived from the above modes.Based on the above technologies and applications,6 security and privacy countermeasures(authentication and access control,privacy-preserving,blockchain-based solutions for data integrity,cryptography and key management,physical countermeasures,and intrusion detection systems)are summarized and discussed.Moreover,the security challenges of smart agriculture are analyzed and organized into two aspects:1)agricultural production,and 2)information technology.Most current research projects have not taken agricultural equipment as potential security threats.Therefore,we did some additional experiments based on solar insecticidal lamps Internet of Things,and the results indicate that agricultural equipment has an impact on agricultural security.Finally,more technologies(5 G communication,fog computing,Internet of Everything,renewable energy management system,software defined network,virtual reality,augmented reality,and cyber security datasets for smart agriculture)are described as the future research directions of smart agriculture.

Automation and digitization of agriculture using artificial intelligence and internet of things

The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity.In most of the countries where the expansion of cropland is merely impossible,agriculture automation has become the only option and is the need of the hour.Internet of things and Artificial intelligence have already started capitalizing across all the industries including agriculture.Advancement in these digital technologies has made revolutionary changes in agriculture by providing smart systems that can monitor,control,and visualize various farmoperations in real-time andwith comparable intelligence of human experts.The potential applications of IoT and AI in the development of smart farmmachinery,irrigation systems,weed and pest control,fertilizer application,greenhouse cultivation,storage structures,drones for plant protection,crop health monitoring,etc.are discussed in the paper.The main objective of the paper is to provide an overview of recent research in the area of digital technology-driven agriculture and identification of the most prominent applications in the field of agriculture engineering using artificial intelligence and internet of things.The research work done in the areas during the last 10 years has been reviewed from the scientific databases including PubMed,Web of Science,and Scopus.It has been observed that the digitization of agriculture using AI and IoT hasmatured fromtheir nascent conceptual stage and reached the execution phase.The technical details of artificial intelligence,IoT,and challenges related to the adoption of these digital technologies are also discussed.This will help in understanding how digital technologies can be integrated into agriculture practices and pave the way for the implementation of AI and IoT-based solutions in the farms.

Computer vision technology in agricultural automation--A review

Computer vision is a field that involves making a machine “see”.This technology uses a camera and computer instead of the human eye to identify,track and measure targets for further image processing.With the development of computer vision,such technology has been widely used in the field of agricultural automation and plays a key role in its development.This review systematically summarizes and analyzes the technologies and challenges over the past three years and explores future opportunities and prospects to form the latest reference for researchers.Through the analyses,it is found that the existing technology can help the development of agricultural automation for small field farming to achieve the advantages of low cost,high efficiency and high precision.However,there are still major challenges.First,the technology will continue to expand into new application areas in the future,and there will be more technological issues that need to be overcome.It is essential to build large-scale data sets.Second,with the rapid development of agricultural automation,the demand for professionals will continue to grow.Finally,the robust performance of related technologies in various complex environments will also face challenges.Through analysis and discussion,we believe that in the future,computer vision technology will be combined with intelligent technology such as deep learning technology,be applied to every aspect of agricultural production management based on large-scale datasets,be more widely used to solve the current agricultural problems,and better improve the economic,general and robust performance of agricultural automation systems,thus promoting the development of agricultural automation equipment and systems in a more intelligent direction.

Deep learning based computer vision approaches for smart agricultural applications

The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies.At the core of artificial intelligence,deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality.Computer vision techniques,in conjunction with high-quality image acquisition using remote cameras,enable non-contact and efficient technology-driven solutions in agriculture.This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting.Recent works in the area of computer vision were analyzed in this paper and categorized into(a)seed quality analysis,(b)soil analysis,(c)irrigation water management,(d)plant health analysis,(e)weed management(f)livestock management and(g)yield estimation.The paper also discusses recent trends in computer vision such as generative adversarial networks(GAN),vision transformers(ViT)and other popular deep learning architectures.Additionally,this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time.The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy.However,the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.

Application status and challenges of machine vision in plant factory—A review

Plant factories have a great potential for mitigating the contradiction between the world’sgrowing population and food scarcity. During the process of its automatic production,machine vision plays a significant role. This technique almost covers every production linkfrom raising seedlings, transplanting, management, and harvesting to fruit grading. To provide references and a starting point for those who are committed to studying this issue. Inthis paper, the application prospects of machine vision in plant factories were analyzed,and the present researches were summarized from the fields of plant growth monitoring,robot operation assistance, and fruit grading. The results found that although the existingmethods have solved some practical problems at low cost, high efficiency and precision,some challenges still are faced by machine vision. Firstly, the changing lighting, complexbackgrounds, and color similarity within plant different parts cause the commonly usedimage segmentation algorithms to fail. The shortage of standard agricultural datasets alsokeeps deep learning and unsupervised classification algorithms from making progress.Secondly, there are some theoretical knowledge gaps for machine vision application in aparticular environment of plant factories, which seriously contains its application effect.Thirdly, the lack of special image acquisition devices and supporting facilities resulted inpoor image quality. All these factors hinder machine vision application in plant factories.Nevertheless, it is still a powerful tool and irreplaceable at present. We believed that thistechnique would promote plant factory development greatly with more robust, efficient,and reliable algorithms are developed in the future.

Comparison of Sick and Hokuyo UTM-30LX laser sensors in canopy detection for variable-rate sprayer

Accurate acquisition of canopy structures,sizes and shapes of plants are significant in providing technical data for variable-rate precision spraying and plant growth estimation in modern agriculture.This research work proposes a comparative analysis of two mainstream brands of laser sensor scanners for canopy detection.A Hokuyo UTM-30LX laser sensor and a Sick LMS151-10100 laser sensor was used to detect spray targets including two artificial trees and a cuboid foam box,respectively.Two data acquisition and storage algorithms based on C++ language have been developed to collect real-time data from the given targets based on the sensors.A 3-dimentional image reconstruction algorithm was proposed to construct the detection targets using MATLAB software.In this experiment,the detection distances between laser sensors and targets range from 1.8 to 2.2 m,and the traveling speed of laser sensors ranges from 0.5 to 2.0 m/s,and the size of trees 2.15×1.24×0.70m^3 have been taken for verification of proposed method.The detection accuracies of both laser sensors have been compared by utilizing the given targets under indoor laboratory conditions,and detection accuracies of 3-dimentional reconstruction images of the target are analyzed by the root-mean-square error(RMSE),the coefficient of variation(CV)and edge similarity score(ESS).The experimental results show that the laser sensors have different detection accuracies under the same experimental conditions,and has different detection accuracy under different experimental conditions.The LMS151-10100 laser sensor is more accurate and more suitable for detection in the case of fast detection speed.However,both sensors have the capability to measure the targets accurately and can be applied for the detection of trees in the area of variable-rate precision spraying.