Precision Agriculture for Crop and Livestock Farming—Brief Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Precision Crop Farming
2.1. Evaluation of Soil Properties by Sensor Measurements
2.2. Precision Seeding
2.3. Smart Irrigation Systems
2.4. Smart Fertilization Systems
2.5. Grass Yield Monitoring
Reference | Application | Involved Technologies | Main Objective/Function |
---|---|---|---|
[10] | Soil management | Soil electrical conductivity sensor | Measures the soil solute concentration while assessing the soil salinity hazard |
[12] | Soil management | Electrodes for frequency domain (FDR) or time domain reflectometry (TDR) | Measures soil water content |
[13] | Soil management | Tensiometer | Detects the force used by the roots in water absorption |
[9] | Soil management | Photodiode | Determines clay, organic matter andmoisture content of the soil |
[14] | Soil management | Ion-selective electrodes (ISE) and ion-selective field effect transistor sensors (ISFET) | Used to detect the primary plant nutrients (NO3, NH4, K and PO4) in soils |
[15] | Soil management | Ground penetrating radar (GPR) and gamma ray spectrometry (GRS) | GPR is related to soil hydrology parameters, and GRS data is related to some soil nutrients and other soil texture characteristics |
[5] | Soil management | GNSS reflectometry | Produce high-resolution maps of soil moisture by the use of drone flying at low altitude |
[18] | Seeding management | Seed drill depth control system | Maintaining of an adequate and uniform seeding depth |
[19] | Seeding management | Electric seeder for small-size vegetable seeds base on power drive and optical fiber detection technology | Perform precision seeding; real-time monitoring the quality of seeding; furrow, seeding and repression at a time |
[20] | Seeding management | Wheel mobile robot for the wheat precision seeding | Wheat precision seeding |
[17] | Seeding management | Control system for seed-metering device using a single chip microcomputer | Make the seed-metering device keep synchronization with the working speed of the seeder |
[21] | Seeding management | Air-assisted high speed precision seed metering device | Solve short filling time issues during high-speed operation; reduce the accumulation of seeds in the venturi tube |
[23] | Water management | Automatic irrigation system | Optimal irrigation strategy for improving the irrigation water use efficiency |
[24] | Water management | IoT based smart irrigation system along with a hybrid machine learning based approach | Predict the soil moisture |
[25] | Water management | Water management system using satellite LANDSAT data and meteo-hydrological modeling | Development of an operational irrigation system for water management |
[26] | Water management | Smart irrigation system using global system for mobile communication (GMS) | Help farmers water their agricultural fields |
[27] | Water management | IoT-based renewable solar energy system | Appropriate actuation command signals to operate irrigation pumps |
[28] | Water management | Low-cost irrigation system based on wireless sensor network using a radio frequency communication. | Make water use and energy consumption more efficient |
[29] | Water management | Smart irrigation system based on real-time soil moisture data | Determine the dynamic designed irrigation depth for guiding irrigation events |
[31] | Fertilizer management | Variable-rate fertilizer control system based on ZigBee technology | Automatically adjust the fertilizer application rate based on a prescription map |
[32] | Fertilizer management | Improved organic fertilizer mixer based on the Internet of Things (IoT) | Monitoring the status of fertilizer production remotely providing updates and alerts to the farmers |
[33] | Fertilizer management | Low-cost agricultural robot (prototype) | Spray fertilizers safely and autonomously; general crop monitoring |
[34] | Fertilizer management | IoT-based fertigation system | Promote sustainable irrigation and fertilization management offering more economic and environmental benefits than empirical models |
[35] | Fertilizer management | Model based on decision support system for agrotechnology transfer (DSSAT) and genetic algorithm | Used to optimize the nitrogen fertilizer schedule of maize under drip irrigation |
[39] | Grass yield management | LiDAR plant height detecting sensor integrated with an active optical NDVI sensor | Estimate of green fraction of biomass in swards comprising both senescent and green material |
[10] | Grass yield management | Spectral reflectance signatures in combination with the ultrasonic sensor | Prediction accuracy of herbage mass from ultrasonic height measurements |
[38] | Grass yield management | Unmanned aerial vehicle-based (UAV) | Acquisition of image data in ultrahigh spatial resolution for important phenological growth stages |
[40] | Grass yield management | Low-cost UAV-based imaging | Prediction of forage yield |
[41] | Grass yield management | Drone-based imaging spectrometry andphotogrammetry | Managing and monitoring of quantity and quality of grass swards used for silage production |
[42] | Grass yield management | On-the-go pasture meter using optical sensors and GPS | Used as a stand-alone sward meter or sward-yield mapping |
[37] | Grass yield management | Grass measurement optimization tool (GMOT) | Development of a spatially balanced and non-biased grass measurement protocol using basic pasture management and geo-spatial information |
2.6. Linking Technology to Farm Machinery
3. Precision Livestock Farming
3.1. Animal Monitoring
3.2. Animal Health and Welfare
3.3. Feed and Live Weight Measurement
3.4. Automatic Milking Systems
Reference | Application | Involved Methods/Technologies | Main Objective/Function |
---|---|---|---|
[89] | Animal behavior | GPS sensors | Tracking location |
[64] | Animal behavior | A neck collar with series of sensors | Detection of estrus events through analysis of rumination rate, and the feeding and resting behavior |
[67] | Animal behavior | Accelerometers in combination with GPS-based data | Discrimination between several kinds of feeding related behaviors for grazing animals Classification of multiple cattle behaviors |
[90] | Animal behavior | A machine learning method | Pig cough detection-processing all incoming sounds and automatically identifying the number of coughs |
[69] | Animal behavior | Cameras and microphones Sound tool based on an algorithm | Find a correlation between vocalization and behavior |
[71] | Animal behavior | A non-invasive imaging system such as VGG-face model, Fisherfaces, and convolutional neural networks | Pig-face recognition |
[74] | Animal health and welfare | Microphones for cough sounds | Detect bovine respiratory disease |
[70] | Animal health and welfare | Air sensors | Prediction the onset of Coccidiosis by monitoring the concentration of volatile organic compounds in the air |
[76] | Animal health and welfare | Algorithm developed through image analysis | Automatic detection of lameness in dairy cows individually |
[80] | Feed management | An automated feeding system | Control the amount of feed provided, and the ambient temperature to optimize animal growth and reduce ammonia emission |
[58] | Feed management | A feed sensor | Measure and control the amount of feed delivered to individual feeders |
[78] | Feed management | A next-generation feeding system | Provide feed with a variety of nutrient specifications to tailor both the amount and composition of the feed |
[79] | Feed management | A computer vision based system CNN models using a low-cost RGB-D camera | Measures cow individual feed intake |
[81] | Feed management | NIRS technology | Evaluation of physio-chemical composition of TMR and manure in dairy farms |
[82] | Weight management | Weighing system based on image analysis | Determine the weight of individual or group of animals (specifically pigs) |
[85,86,87,88] | Automatic milking systems | Time of flight (ToF) depth sensing cameras Algorithmic solutions from depth images and point-cloud data Machine learning based vision for smart MAS Combination of thermal imaging and stereovision techniques | Teat Detection Teat detection and tracking Capability for faster and accurate teat detection Teat sensing |
4. Risks and Concerns about Precision Farming
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Monteiro, A.; Santos, S.; Gonçalves, P. Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals 2021, 11, 2345. https://doi.org/10.3390/ani11082345
Monteiro A, Santos S, Gonçalves P. Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals. 2021; 11(8):2345. https://doi.org/10.3390/ani11082345
Chicago/Turabian StyleMonteiro, António, Sérgio Santos, and Pedro Gonçalves. 2021. "Precision Agriculture for Crop and Livestock Farming—Brief Review" Animals 11, no. 8: 2345. https://doi.org/10.3390/ani11082345