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Smart Farming

Smart Farming - Opportunities for plant, livestock and agricultural equipment-related applications

This report explores technological innovations in the agriculture landscape, encompassing three distinct categories:

Plant-related applications
Livestock-related applications
Machinery and equipment-related applications

After an extensive analysis of the main use cases and expected take-up rates, it takes a detailed look at how the players are positioned in each category.
The report further analyses the market dynamics and quantitative opportunities of each category and among the major geographical zones.


1. Executive summary
1.1. Key findings
1.2. Recommendations
1.2.1. Plant management
1.2.2. Livestock
1.2.3. Equipment and machinery applications
2. Key issues and medium-term industry perspectives
2.1. Opportunities
2.2. Threats
3. Plant-related applications
3.1. Main solutions
3.1.1. Hardware
3.1.2. Connectivity
3.1.3. Services, software and analytics
3.2. Market structure and adoption
3.2.1. Key players and their positioning on each market
3.2.2. Market adoption
4. Livestock-related applications
4.1. Main solutions
4.1.1. Hardware
4.1.2. Connectivity
4.1.3. Services, analytics, IA and big data
4.2. Market structure and adoption
4.2.1. Key players and their positioning on each market
4.2.2. Market adoption
5. Equipment-related applications
5.1. Main solutions
5.1.1. Hardware
5.1.2. Connectivity
5.1.3. Services and analytics
5.2. Market structure and adoption
5.2.1. Key players and their positioning on each market
5.2.2. Market adoption
5.2.3. Drivers and barriers
6. Market analysis
6.1. Market dynamics
6.1.1. Drivers
6.1.2. Barriers
6.2. International adoption
6.3. Market sizing
List of tables and figures
Tables
Table 1: Input cost per hectare for large-scale crop cultivation (wheat, maize, colza, sunflower, barley) in France
Table 2: Network requirements of plant-related applications in agriculture
Table 3: Use of collected data
Table 4: Evapotranspiration factor by type of crop, by growth stage
Table 5: Growing Degree Day for certain crops to reach maturity
Table 6: Leaf Wetness Duration indicator
Table 7: Positioning of some companies providing plant-related applications in agriculture
Table 8: Providers of connected equipment
Table 9: Drone manufacturers and service providers offering precision agriculture service
Table 10: Robots designed for agriculture
Table 11: Examples of solutions providers relying on LPWA connectivity
Table 12: Drivers and barriers for the development of plant-related applications
Table 13: Example of pricing for connected livestock-related solutions
Table 14: Network requirements of livestock-management
Table 15: Positioning of key players in animal-related applications
Table 16: Connectivity types used for livestock applications
Table 17: Drivers and barriers for the development of livestock-related applications
Table 18: Requirements of different equipment-related applications
Table 19: Drivers and barriers for the connected equipment market
Figures
Figure 1: Per capita dairy consumption and population, world
Figure 2: Typical margin levels on the farming, agriculture and food value chain
Figure 3: Types of parameters to be monitored in plant-related applications
Figure 4: Soil sensors at different depths for vineyards
Figure 5: The principle of wireless soil sensors
Figure 6: Sap flow sensors to measure vine stress
Figure 7: Agriculture weather station
Figure 8: Installation pattern of sensors in vineyard
Figure 9: Connected water tank
Figure 10: Quanturi Haytech temperature sensors
Figure 11: BeanIoT concept
Figure 12: Vigour map
Figure 13: Principle of thermal sensors embedded on drones
Figure 14: Drone-based NDVI mapping (left); Location or sections affected by rust fungus in red in a wheat crop (right)
Figure 15: Drone use per growth stage
Figure 16: Precision spraying by drones
Figure 17: Bonirob, a weeder by Bosch Deepfield Robotics
Figure 18: VINBOT robot prototype
Figure 19: Insights from data
Figure 20: Example of insights expected by farmers
Figure 21: Yield impacts of trunk diseases, by vineyard age
Figure 22: Example of services limited to an app (left panel) and more advanced functions (right)
Figure 23: My. Luda.Farm unique interface to monitor several checkpoints
Figure 24: Prospera software monitoring tomato crops
Figure 25: Value chain of plant-related applications in agriculture
Figure 26: Water status according to the heterogeneity of each plot
Figure 27: Connected hay and straw solution from Quanturi
Figure 28: Javelot sensor and gateway offering
Figure 29: Example of map compiled from a drone flight
Figure 30: Weeding robot within vegetable plots
Figure 31: Example of results from ‘itk’ solution: effective rooting depth visualisation
Figure 32: FARMSTAR application of satellite imagery on plants
Figure 33: Type of data collected from a cow
Figure 34: Impact of lying time on lactation production
Figure 35: Connected bolus used for rumen monitoring
Figure 36: MooMonitor+ connected necklace, describing a cow’s health status
Figure 37: Detection of oestrus (heat) incidence in cows, using a connected device
Figure 38: Monitoring a cow’s responsiveness to a teaser bull
Figure 39: DeLaval 24-stand automated milking system
Figure 40: Mastitis sensors
Figure 41: Dynamic 24/7 feed pushing
Figure 42: Livestock theft in Australia in 2017 (and estimated value loss)
Figure 43: Sheep tracking with collar
Figure 44: Animal body temperature image captured by drone
Figure 45: Typical configuration for animal health monitoring
Figure 46: Example of connectivity configuration and range of a connected bolus
Figure 47: Connected device operations
Figure 48: Cainthus facial recognition system
Figure 49: Value chain of animal-related applications in agriculture
Figure 50: Fragmentation of the market (illustrative)
Figure 51: Copeeks device monitoring
Figure 52: Moocall HEAT value proposition
Figure 53: Lely T4C farm management software
Figure 54: Cattle Watch configuration using cellular and satellite connectivity, depending on coverage
Figure 55: Digitanimal herd monitoring solution
Figure 56: Main applications for connected equipment
Figure 57: Operating principle of a self-guided RTK system
Figure 58: AutoTrac kit for non-Deere machines
Figure 59: Receiver for the AutoTrac system
Figure 60: Smart spraying (connected machinery)
Figure 61: Field manager from Xarvio (Bayer)
Figure 62: Connectivity implementation techniques
Figure 63: Connected tracker for equipment geolocation
Figure 64: Subscription price for a connected tracker
Figure 65: Remote diagnosis via cellular network
Figure 66: John Deere connect mobile
Figure 67: Mobile application of JDLink service for John Deere tractors
Figure 68: Go-to-market approach at Claas
Figure 69: Value chain of the connected tractor market
Figure 70: Market share of tractor manufacturers, 2016
Figure 71: AgCommand ( Massey Ferguson)
Figure 72: Optional C3000 console for Auto-Guide 3000
Figure 73: Value chain of machinery equipment market
Figure 74: Share of farm equipment with precision agriculture component
Figure 75: Blossoming interest in agricultural technology – worldwide funding in agtech start-ups
Figure 76: Precision agriculture technology usage in 2015
Figure 77: Evolution of the installed base in agriculture market, in million units, per segment, 2018-2025
Figure 78: Evolution of the installed base in agriculture market, in million units, per region, 2018-2025

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