Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture
Abstract
:1. Introduction
- World prospects for agriculture advancement;
- Technology-based limitations faced by the agricultural industry, and the role of major equipment and technologies, such as the IoT, and UAVs, to address these limitations and other issues, such as resource shortages and their precise uses, food quality, environmental pollutions, climate change, and urbanization;
- Highlighting the latest developments in the IoT and other technologies that support advanced agriculture;
- Strategies and policies to consider when implementing the IoT and other technologies in advanced agriculture;
- Key issues in food safety with recommendations to address those issues;
- Future prospects and recommendations of these advanced technologies.
2. Methods
3. Major Agriculture Applications and Services
3.1. Soil Monitoring
3.2. Irrigation
3.3. Crop Disease and Its Management
3.4. Fertilizer
3.5. Crop Harvesting Monitoring and Forecasting
4. Advanced Agricultural Approaches
4.1. Greenhouse Agriculture
4.2. Hydroponics
4.3. Vertical Farming (VF)
4.4. Phenotyping
5. Major Equipment and Technologies
5.1. Smartphone
5.2. Agricultural Communication
5.2.1. Cellular Communication
5.2.2. Bluetooth
5.2.3. Zigbee
5.3. Sensor Devices
5.4. Advanced Machines Used to Advance Agriculture
5.5. Cloud Computing
5.6. Harvesting
6. Uses of UAVs in Agriculture Advancement
6.1. Soil and Water Analysis
6.2. Planting
6.3. Irrigation
6.4. Health Assessment of the Crops
6.5. Spraying Pesticides/Herbicides
6.6. Plant Species Detection/Identification
7. Food Safety and Transportation
7.1. Compliance
7.2. Laird Sentrius
7.3. Tempreporter
7.4. CCP Smart Label (RC4)
8. Current Challenges and Future Prospects
8.1. Smartphone and the IoT
8.2. The IoT and Wireless Sensors (IoTWS)
8.3. Communication
8.4. Drones and Other Robots
8.5. Machine Learning and Artificial Intelligence
8.6. Energy Consumption, Renewable Energy, Microgrid, and Smart Grid
8.7. Vertical Farming and Hydroponics
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Smartphone Sensors | Purpose | Common Agriculture Usages | References |
---|---|---|---|
Image sensor (Camera) | Take images of any object | Leaf area index (LAI), fruit ripeness, harvest readiness, soil erosion, disease detection. | [87] |
Accelerometer | Measures acceleration that used to observe the tilting motion and orientation of the object | Rotation of camera during use, machine activities, or detecting workers. | [88] |
GPS | Provides location, measuring the latitude and longitude of devices | Used for location information, land measurement, and crop mapping | [89] |
Microphone | Detects usual or unusual sound and transform into electrical waves | Maintenance of machine, detection of bugs, and making audio queries. | [90] |
Gyroscope | Senses the angular velocity to track the target rotation/turn | Canopy structure measurement and equipment movement. | [89] |
Inertial Sensor | Utilizes accelerometer and gyro to determine the object altitude in relation to the inertial system | The precise distance of the plant, leaves, and/or any other object is measured from the camera. | [91] |
Barometer | Measure air pressure | Measure air pressure | [92] |
Mobile Apps | Application | Feature/Achievement | References |
---|---|---|---|
PETAFA | GIS | It provides information on the normalized difference vegetation index (NDVI) for different crops at various life cycles. However, it distributes geo-referenced soil analysis through packages. | [5] |
LandPKS | Soil Assessment | Land management has long-term potential, depending on weather, topography, and relatively static soil properties (such as depth, soil texture, and mineralogy). The app aims to increase growers’ understanding of the land potential and climate change adaptation and mitigation activities. | [93] |
PocketLAI | Irrigation | The app estimates the leaf area index (LAI), which is the main factor determining plant water requirements. It uses a moving camera and accelerometer sensor to acquire images at 57.5° under the hood while the user keeps rotating the device along its central axis. | [94] |
AMACA | Machinery or Devices | Equipment costs are a significant part of crop expenditure. The application helps estimate the mechanical and implantation costs in different field operations. Follow the cutter-driven quality function deployment (QFD) approach to meet your expectations with user expectations for application design features. | [95] |
eFarm | GIS | eFarm is crowdsourcing and human perception tool that collects geo-tagged agricultural land information at the parcel level. Ideal for mapping, sensing, and modeling of agricultural land systems research. | [84] |
Ecofert | Management of Fertilizers | Ecofert helps manage the best use of fertilizer files. It calculates the best fertilizer combination based on the required nutritional solution and considers the needs of different crops. In addition, it considers fertilizer costs based on current market prices. | [96] |
AgriMaps | Land Management | The application follows an evidence-based, site-specific approach to make recommendations for cropland management. Compared to other related applications, it provides a platform for spatial data visualization with a wider range of geospatial information. | [97] |
SWApp | Irrigation | The developers of this app specifically targeted arid regions, as irrigation problems are more common in these regions. The application provides a reliable and economical solution for monitoring soil moisture and even considers weather history. | [98] |
SnapCard | Sparing applications | The SnapCard application was developed for the field analysis of spray collectors based on imaging analysis. It uses different cell phone sensors and follows five imaging methods to quantify droplet deposition and size. | [99] |
Weedsmart | Weed Management | This app can increase weed management in the pasture. Based on the answers given to nine questions about pasture farming systems, this application assesses herbicide resistance and the risk of weed seed banks. | [100] |
Village Tree | Pest Management | Village Tree provides smart pest management solutions by collecting plant pest and disease reporters. It uses a crowdsourcing method and sends images along with location knowledge to warn other growers that may be affected. | [101] |
cFertigULF | Fertigation | The tool measures the amount of fertilizer and water required for major crop types based on different crop growth systems and multiple fertilization techniques. Farmers can achieve the precise application of water and other nutrients in greenhouse farming. | [102] |
Communication/Data Type | Possible Applications | Expected Data Size | Power Consumption (Active Mode) |
---|---|---|---|
Small-sized data and power consumption | Air temperature and/or wind speed, soil, leaf thickness/color (chlorophyll), fruit size, flower | 100 s of bytes | Less than an mA (fraction of mA) |
Medium-sized data medium power consumption | Multi/hyper spectral camera, Acoustic sensors | 10 s of Mb | 10 s of mA |
Large size data and power consumption | Video streaming cameras | 10 s of Mb per minute | 50 A |
Wireless Sensors | Employment | References |
---|---|---|
Telematics Sensors | Telematics sensors are the leading equipment used for communication, an agricultural-based toolkit, which is the most accurate and precise communication tool. This application is mainly used to gather information from remote areas that are not accessible easily, report the information of the machine’s working status, collect information about areas, locations, and assist in locating travel routes. These programs help farming managers automatically store and record information correlated with agriculture. | [109,110,111] |
Remote Sensing | Remote sensing tools are used to capture and store geographical information and several environmental and climatic parameters. Moreover, it helps in managing, manipulating, displaying, and analyzing geographical and spatial information. These sensors help assess several factors, such as forecasting, monitoring, yield assessment, crop evaluation, land degradation, and pest management (e.g., using LiDAR, satellite, UAVs). For example, the Argos sensor can be used for processing, disseminating, and collecting global-based data and is compatible with smartphone platforms. | [112,113,114,115,116] |
Acoustic Sensor | Acoustic sensors provide other tools for farm management, such as weeding and fruit harvesting. The major advantage of this advanced technology is its low price with quick response capabilities, particularly when considering convenient devices. | [33,117,118] |
Light Detection and Ranging (LIDAR) | This technology is utilized in different agricultural applications, including segmentation, land mapping, farm 3D models, determining soil types, yield prediction, soil loss, and monitoring erosion. Moreover, LiDAR is also utilized to monitor dynamic measurements, such as leaf area and fruit. | [119,120,121,122,123,124] |
Optical Sensor | These sensors use the phenomenon of light reflection to help measure soil organic matter, soil moisture, and color, the presence of minerals and their composition, clay content, etc. These sensors can be used to evaluate the soil’s ability to reflect light based on different parts of the selector’s magnetic field. | [125,126] |
Ultrasonic Ranging Sensor | This type of sensor can be one of the best choices in various agricultural applications because of its low price. It is easy to use, and its sampling rate can easily be adjusted/modified. Frequent uses are tank monitoring, spray distance measurement (for example, boom height and width control for uniform spray reporting, object detection, and collision avoidance), and crop canopy monitoring. | [127,128,129] |
Optoelectronic Sensor | Optoelectronic sensors can distinguish plant types; hence, they help to detect weeds, and other plants, particularly in wide-row crops. Optoelectronic sensors are also capable of differentiating between vegetation and soil from their reflection spectra. | [130] |
Electromagnetic Sensor | Electromagnetic sensors are used to record conductivity and transient electromagnetic responses, identify electrical responses and adjust variable-rate applications in practical situations. Sensors based on this technology use electrical circuits to measure the ability of soil particles to conduct or accumulate charge, which is mainly accomplished by two methods; contact or non-contact. | [131] |
Electrochemical Sensor | This is used to assess soil characteristics to analyze the soil’s nutrient level, for example, pH. Standard chemical soil assessment methods are often time-consuming and expensive and can be simply replaced with these advanced sensors. These sensors are used to measure macro and micronutrients, salinity, and pH in the soil precisely. | [132,133] |
Mechanical Sensors | Mechanical sensors evaluate the mechanical resistance (compaction) of the soil to indicate variable compaction. Mechanical sensors enter or pass through the soil and record forces evaluated by strain gauges or load cells. | [134] |
Airflow Sensor | These sensors measure the soil’s permeability and moisture content and identify the soil structure to distinguish different soil types. Measurements can be made in a single position or dynamically during movement, for example, in a fixed position or mobile mode. | [135] |
Mass Flow Sensor | This sensor is used for yield monitoring because it provides yield information by measuring the amount of grain flow (for example, when passing through a combine harvesting). | [136,137] |
Eddy Covariance-Based Sensor | This sensor can be utilized to quantify the exchange of water vapor, carbon dioxide, methane, and other hydrologic and climatic parameters. This eddy covariance technique provides a robust technique to quantify the gas fluxes among soil, vegetation, and atmosphere, which are essential for most agricultural applications in various ecosystems. | [134,138] |
SWLB Sensor | Soft water level-based (SWLB) sensors are utilized in advanced agricultural watersheds to monitor hydrological behavior, including flow and water level, inflexible time-step acquisitions. | [9,136] |
Organization | Initiatives and Vision | References |
---|---|---|
In order to provide food heating systems, Google and the MIT Media Lab Open Agriculture Initiative (Open AgTM) proposed a vision for future agriculture and crops. To give advanced cloud-based services in advanced agriculture the program (e.g., Food ComputerTM equipment) and many open-source technologies in closed and climate-controlled environments. They also proposed different initiatives, such as its Climate Recipe Program, which proposed solutions based on the cross-correlation of plant phenotypic responses with biological, environmental, and genetic variables. | [176] | |
Microsoft | Microsoft has begun to invest in advanced agriculture. The company started a five-year, USD 50 million plan in 2018 named Al for Earth. In this plan, Microsoft targets four key areas for building a suitable future: agriculture, climate, water, and biodiversity. The primary goal of the company is to use its expertise in cloud computing, artificial intelligence (AI), and the internet to solve agricultural problems. | [177] |
Intel | Infiswift is an IoT platform based on a high-performance Intel architecture that aims to improve the efficiency of agricultural operations by providing connected services throughout the agricultural ecosystem. | [5] |
Jasper, Cisco | Jasper is part of Cisco and provides a cloud-based software platform for agribusiness IoT. The platform is rapidly embracing IT services to realize advanced agriculture using automation, real-time visibility, and remote diagnoses. | [178] |
Watson, International Business Machines (IBM) | Watson Decision is an AI-based service that delivers an agricultural platform designed to use advanced equipment and IT to develop the sustainability, harvest, and value of advanced agriculture. In this way, IBM uses its experience, data, and AI services to support growers in making excellent decisions throughout the planting stage. | [179] |
Hewlett Packard Enterprise (HPE) | Purdue University has begun to use wireless sensor innovation and the IoT to revolutionize agricultural research; every day, different sensors, cameras, and different types of manual input are used to capture essential data, all of which are processed and evaluated in real-time. In order to effectively monitor food quality, Purdue cooperates with HPE to integrate research, innovation, and technology such as cloud computing and internet technology to transform into the latest practice of digital agriculture. | [180] |
Dell | Dell has begun to introduce agricultural robots and machines equipped with advanced machine learning and AI functions. The company has currently joined Aero-farms (vertical agricultural power) to accelerate the provision of the IoT and data science services for advanced agriculture. | [181] |
Qualcomm | Qualcomm Ventures (QV) has been one of the leading wireless companies for the past 15 years, and now QV considers AgTech to be one of the main investment areas for future projects. They have recently established global partnerships with Strider (Brazilian Farm Management Platform), Ninjacart (Indian Agricultural Comprehensive Trading Market), and FarmEasy (Chinese Farm Data Platform), especially in Latin America Partnership reveals the status of advanced agriculture. | [182,183] |
Hello, Tractor | Hello, Tractor and IBM Research have established a blockchain-based platform AI that pays special attention to African growers. The new technology giant co-founded by IBM will jointly test the product this year. The cloud-based service, dubbed Digital Wallet, aims to support the Hello Tractor business, which is dedicated to providing small-scale farmers with technical equipment and analytical data to create advance agricultural ecosystems. | [184] |
Farm2050 | According to Farm2050, worldwide food production has to be increased by 70% from the current levels to meet the increased food demand for a population of approximately 10 billion by 2050. This is a major initiative for AgTech in the future, as 25 world-leading organizations such as Google, Microsoft, Bayer, John Deere, and Pepsi cooperate with this organization. Its rudimentary objective is to use new technologies to develop the future of food by supporting AgTech entrepreneurs and start-ups. | [185] |
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Khan, N.; Ray, R.L.; Sargani, G.R.; Ihtisham, M.; Khayyam, M.; Ismail, S. Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture. Sustainability 2021, 13, 4883. https://doi.org/10.3390/su13094883
Khan N, Ray RL, Sargani GR, Ihtisham M, Khayyam M, Ismail S. Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture. Sustainability. 2021; 13(9):4883. https://doi.org/10.3390/su13094883
Chicago/Turabian StyleKhan, Nawab, Ram L. Ray, Ghulam Raza Sargani, Muhammad Ihtisham, Muhammad Khayyam, and Sohaib Ismail. 2021. "Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture" Sustainability 13, no. 9: 4883. https://doi.org/10.3390/su13094883