The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture
Abstract
:1. Introduction
2. Precision Agriculture Approaches, Applications, and Impacts
2.1. Data Collection and Acquisition
2.2. Planning, Decision Making, and Execution
3. Precision Agriculture: The Next Frontier for Sustainable Farming
3.1. Big Data
3.2. Machine Vision Technology
3.3. Internet of Things (IoT)
3.4. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)
3.5. Guidance Systems
3.6. Blockchain Technology
3.7. Robotics and Autonomous Systems
3.8. Artificial Satellites, Unmanned Aerial Vehicles (UAVs), and Unmanned Ground Vehicles (UGVs)
3.9. High-throughput Phenotyping
3.10. Telematics
4. Studies of Successful Precision Agriculture Proposals and Implementations
Exploration | Location | Technology Used | References |
---|---|---|---|
Usage of Smart Contracts with FCG for Dynamic Robot Coalition Formation in Precision Farming | St. Petersburg, Russia | IoT, agricultural robotics, blockchain technology with hyperledger fabric platform | [7] |
A mobile lab-on-a-chip device for on-site soil nutrient analysis | Vienna University of Technology, Vienna, Austria | Micro-chip capillary electrophoresis sensor device | [71] |
Development and test of an electric precision seeder for small-sized vegetable seeds | Henan University of Science and Technology, Luoyang, China | Optical fiber detection technology | [70] |
Smart irrigation forecast using satellite LANDSAT data and meteo-hydrological modeling | Politecnico di Milano, Milan, Italy | IoT sensors | [73] |
IoT solar-energy-powered smart farm irrigation system | American University of Sharjah, Sharjah, United Arab Emirates | Chip controller with built-in WiFi connectivity, IoT | [77] |
Autonomous fertilizer mixer through the Internet of Things (IoT) | University Tenaga Nasional, Selangor Darul Ehsan, Malaysia | IoT | [68] |
Design and development of a robot for spraying fertilizers and pesticides for agriculture | University Tenaga Nasional, Selangor Darul Ehsan, Malaysia | Agricultural robots | [67] |
25 years of Precision Agriculture in Germany—A retrospective | Federal Research Institute for Cultivated Plants, Bundesallee, Braunschweig | Computer-aided farming, IoT-based pH sensor, VRT | [11] |
Field Evaluation of a Variable Rate Aerial Application System | United States Department of Agriculture, Texas, USA | UAVs, VRT, high-resolution camera | [23] |
A harvesting robot system for cherry tomatoes in greenhouses | Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China | Agricultural robots | [69] |
Characterization of Tree Composition using Images from SENTINEL-2: A Case Study with Semiyang oreum | Republic of Korea | SENTINEL-2 satellite, image analysis, remote sensing, | [17] |
Innovation in the Breeding of Common Beans Through a Combined Approach of in vitro Regeneration and Machine-Learning Algorithm Citation | Sivas, Turkey | ML and ANN models | [44] |
3D-Printed Prolamin Scaffolds for Cell-Based Meat Cultures | Suzhou, Jiansu, China | 3D-printing technology, high-precision microstructures for biomedical applications | [74] |
Construction of 3D-printed meat analogs from plant-based proteins: Improving the printing performance of soy protein- and gluten-based pastes facilitated by rice protein | Nanchang, China | 3D-printing technology | [75] |
Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains | Qilian Mountains, China | SENTINEL-2 images | [14] |
Detection of flood disaster system based on IoT, big data, and convolutional deep neural network | Sairam Institute of Technology, India | CDNN classifier, ANN, DL, deep-learning neural network (DNN) | [30] |
A multisensor data fusion approach for creating variable depth tillage zones | Newbury, UK | VRT | [27] |
A Data Fusion Method for Yield and Soil Sensor Maps | Veris Technologies Inc., Kansas, USA | IoT, GPS, soil data maps, yield data maps | [21] |
Computer Vision and Deep-learning-enabled Weed Detection Model for Precision Agriculture | Computer vision, DL, IoT, smartphone | [25] | |
Short Communication: Spatial Dependence Analysis as a Tool to Detect the Hidden Heterogeneity in a Kenaf Field | Jeju National University kenaf-breeding field, Jeju, Republic of Korea | LISA analysis | [22] |
Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging | Kyungpook National University, Daegu, Republic of Korea | Hyperspectral transmission imagery, multispectral camera, Python | [32] |
Field road classification for GNSS recordings of agricultural machinery using pixel-level visual features | Beijing, China | GNSS | [46] |
A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture | Alma Mater Studiorum University of Bologna, Bologna, Italy | UAV, GIS, ML | [45] |
Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario | Campus de Santa Apolónia, Bragança, Portugal | UAV | [49] |
Drought Stress Restoration Frequencies of Phenotypic Indicators in Early Vegetative Stages of Soybean (Glycine max L.) | Rural Development Administration, LemnaTec, Germany | RGB images, Python | [57] |
Durian Farmer Adoption of Smart-Farming Technology: A Case Study of Chumphon Province | Kasetsart University, Bangkok, Thailand | IoT, UAV | [76] |
5. Barriers to Adapting New Technologies in Precision Agriculture
Advantages | Limitations | Main Applications | |
---|---|---|---|
Big Data | Data-driven insights Resource optimization Enhanced decision making [1,78] | Robust data management infrastructure Data privacy and security considerations Challenges in integrating heterogeneous data sources [8,78] | Crop yield forecasting Disease and pest management Precision agriculture Predictive analytics Farm management systems [1,6,8] |
Machine Vision Technologies | Automated image capture and analysis Enhanced efficiency Reduction of reliance on manual labor Precise monitoring of plant health | Dependence on high-quality images Challenges in image interpretation under varying lighting and environmental conditions | Crop monitoring Disease detection Quality assessment Plant phenotyping Weed detection Yield estimation [8] |
IoTs (Internet of Things) | Real-time monitoring Facilitation of data-driven decision making Optimization of resource usage Early detection of issues [78] | Requires reliable network infrastructure Data management and integration challenges Maintenance of hardware [8,16] | Precision agriculture Smart irrigation systems Livestock monitoring Environmental sensing Fishery management Remote farm management [8,13,16,78] |
Artificial Intelligence (AI) | Automation and predictive analytics of decision support systems Enhancment of crop management, disease detection, and yield optimization [16,85] | Requires large data sets Computational resources Challenges in explainability and interpretability of AI models | Crop yield prediction Disease detection Pest management Image recognition Mobile expert systems Anomaly detection [8,85] |
Machine Learning (ML) | Enables pattern recognition Predictive modeling Data analysis Assists in crop disease diagnosis, yield prediction, and recommendation systems [6,14] | Requires labeled training data, model training, and optimization Potential bias in algorithmic decision making [6,28] | Crop disease diagnosis Yield prediction Soil analysis Yield optimization Breeding optimization Farm management systems [6,28,44] |
Deep Learning | Complex pattern recognition Analysis of large data sets Suitable for image and signal processing tasks, disease detection, and plant phenotyping [25,31] | Requires substantial computational resources Large labeled data sets Potential overfitting with limited data [31,86] | Plant disease detection Plant classification Object recognition Plant phenotyping Image-based analysis [25,31,86] |
Guidance Systems | Precise navigation and operation of agricultural machinery Reduces overlaps and optimizes resource usage [47] | Requires accurate positioning systems Potential dependency on external signals Challenges in complex terrains [78] | Precision agriculture Automated field operations Autonomous machinery Variable rate application [34,47] |
Blockchain Technologies | Provides transparency, traceability, and secure data sharing in the agricultural supply chain Enables trust, verification, and fair transactions | Scalability challenges Energy consumption Integration complexity | Supply chain management Food traceability Quality assurance Fair trade [8,16] |
Robotics and Autonomous Systems | Enables automation, precision tasks, and labor reduction Assists in autonomous field operations, weeding, harvesting, and data collection [63,78] | Cost of implementation Limited adaptability to changing field conditions Detection accuracy and technical challenges in complex environments [8,63] | Automated harvesting Weeding Field monitoring Planting Labor-intensive operations [8,34,63] |
UAVs (Unmanned Aerial Vehicles) | Remote sensing Aerial imaging Monitoring of large agricultural areas Provides timely data collection Improved field management Cost-effective crop assessment [34,78,86] | Restricted flight regulations Limited payload capacity Challenges in data analysis and interpretation Expensive and break easily [14,34] | Crop monitoring Mapping Aerial imaging Precision agriculture Disease detection [8,34,76] |
Unmanned Ground Vehicles | Ground-level monitoring Data collection Field operations in various terrains Assists in precision spraying, mapping, and soil sampling | Limited mobility in challenging environments Dependence on stable terrain conditions | Precision spraying Soil sampling Field mapping Data collection [49,78,86] |
High-Throughput Phenotyping | Facilitates rapid and non-destructive measurement of plant traits and characteristics Enhances breeding programs, genetic analysis, and crop improvement [56] | Cost of high-throughput phenotyping platforms Challenges in data interpretation Standardization of measurement protocols | Plant breeding Crop improvement Stress tolerance assessment Genetic analysis Trait selection [56,71] |
Telematics | Enables real-time monitoring, tracking, and data collection from vehicles Enhances fleet management, route optimization, and driver safety | Requires reliable connectivity Potential data security concerns Challenges in integrating with existing vehicle systems | Fleet tracking Logistics management Fuel efficiency analysis Predictive maintenance Driver behavior monitoring [2] |
6. Future Developments Required
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Sigfox | LoRaWAN | NB-IoT | Zigbee | Wi-Fi | 5G | |
---|---|---|---|---|---|---|
Bandwidth | Low bandwidth | Low to moderate bandwidth | Low to moderate bandwidth | Low to moderate bandwidth | High bandwidth | Very high bandwidth |
Maximum Data Rate | Up to 100 bps | Up to 27 kbps | Up to 250 kbps | Up to 250 kbps | From a few Mbps to several Gbps (varies based on the version) | High data rates from several hundred Mbps to multi-Gbps |
Payload Length | Limited to 12 bytes per message (140 messages per day) | Up to 51 bytes per message (varies depending on the region) | Up to 1600 bytes per message (varies depending on the network operator) | Up to 128 bytes per message (varies depending on the network layer) | Up to several kilobytes per message (varies based on the version) | Supports large payload sizes ranging from several kilobytes to several megabytes |
Coverage | Several kilometers in rural areas and up to a few hundred meters in urban areas from a Sigfox base station | Varies from a few kilometers in urban area and tens of kilometers in rural areas depending on antenna height and line of sight | Wide area of coverage up to several kilometers or more from a base station by leveraging existing cellular infrastructure (similar to 2G/3G cellular networks) | Up to tens of meters (can be extended by utilizing mesh networking, allowing devices) | Limited to indoor around 30–50 m or local area environments (can be extended) | A few hundred meters to several kilometers from a base station (varies depending on the frequency band and deployment strategy) |
Cost | Relatively low cost due to its simple infrastructure requirements | Cost-effective due to shared infrastructure and low-power devices | Affordable due to utilizing existing cellular infrastructure | Reasonably priced, especially for small-scale deployments | Cost-effective for local area networks, but infrastructure costs can vary | Higher infrastructure costs compared to other technologies |
Advantages | Low power consumption, long-range coverage, low-cost infrastructure | Long-range coverage, low power consumption, low-cost infrastructure | Wide network coverage, secure, supports voice and mobility | Low power consumption, mesh networking, supports large networks | High bandwidth, widespread availability, support for various applications | Very high bandwidth, ultra-low latency, massive device connectivity, high reliability |
Disadvantages | Limited bandwidth, low data rate | Limited bandwidth, shared spectrum, higher latency | Higher power consumption compared to other LPWAN technologies | Limited range, interference from other devices, complex network setup | High power consumption, shorter range, limited scalability | Higher infrastructure cost, limited coverage in some areas, higher power consumption |
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Karunathilake, E.M.B.M.; Le, A.T.; Heo, S.; Chung, Y.S.; Mansoor, S. The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Agriculture 2023, 13, 1593. https://doi.org/10.3390/agriculture13081593
Karunathilake EMBM, Le AT, Heo S, Chung YS, Mansoor S. The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Agriculture. 2023; 13(8):1593. https://doi.org/10.3390/agriculture13081593
Chicago/Turabian StyleKarunathilake, E. M. B. M., Anh Tuan Le, Seong Heo, Yong Suk Chung, and Sheikh Mansoor. 2023. "The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture" Agriculture 13, no. 8: 1593. https://doi.org/10.3390/agriculture13081593