Digitech Value Chain
The “Digitech Value Chain inspiration framework for Agrofood, Forestry and Environment” identify the main transnational challenges and the technological trend affecting the Agrofood, Forestry and Environment value chains. These challenges will set the boundaries for the presentation of potential ICT-based solutions that can have a positive effect on the sectors under assessment.
The “Digitech value chain inspiration framework” allows ICT SMEs to identify agri-food market opportunities.
The data sources are diverse and pre-processing must be done in order to have the data in the right format depending on the tasks under study. This relates to standardization and novel interoperability mechanisms for sharing data from multi-sources.
Blockchain takes in Big Data is more secure, as the data cannot be forged or falsified. In the agriculture domain, smart contracts together with automated payments; agricultural insurance, green bonds, and traceability could be the game-changer.
A robot can use 90% less herbicide, making it 30% cheaper than traditional treatments. A fleet of these robots could easily replace human farm labor down the road. Fruit picking robots, driverless tractor/sprayers, and sheep shearing robots are designed to replace human labor. Robots can be used for other horticultural tasks such as pruning, weeding, spraying and monitoring. As well as in livestock applications such as automatic milking, washing and castrating. They can also be used to automate manual tasks, such as weed or bracken spraying, where the use of tractors and other manned vehicles is too dangerous for the operators. Automated Guided Vehicles can increase the precision of agricultural operations. For example, autonomous tractors with intelligent agricultural tools, can reduce soil compaction and reduce the overdosage of nitrogen and herbicides.
The sensor technology evolution is generating cheaper sensors for several relevant agronomic parameters, such as NPK quantification, soil moisture, soil temperature, rainfall, wind, sunlight, chlorophyll concentration Index, leaf wetness, air temperature, etc.; which allows better decisions with an expected impact on the efficient use of the resources.
IoT will play an essential role on intelligent and precision agriculture/forestry, namely to collect data more accurately and allow a precision control on the field, to reduce water and energy costs and improve operations efficiency. IoT can innovate and interconnect irrigation systems, crop data collection, climate conditions monitoring, greenhouse automation, Crop management, Cattle monitoring and management, operations monitoring and control, transport management systems and end-to-end farm management systems. IoT depends on Lora, Sigfox, Wifi, 4G, 5G networks and, as other solutions, their implementation still suffers from traditional challenges such as a lack of or poor infrastructure, failures of interoperability, and other technological issues.
DSS should provide valuable information to support decision-making concerning the management of the resources available such as water, machinery, workers. The plans and schedules for performing operations along the value chain are often displayed in the form of user-friendly interfaces. Dashboards and other monitoring features enable remote follow-up of operations execution when integrated with IoT. DSS implies the design and implementation of information systems that can integrate machine learning, predictive analytics, big data and optimization approaches as back-office tools.
ML can be used in many tasks related to agriculture since data can be collected. These data can be images, data from sensors, data obtained from soil analysis, from meteorological stations, from productivity cards, measures collected in plant breeding projects, among others. These data can be used to generate production predictions or potential hydric predictions, to detect plagues, to recommend treatments given the place and the time, among several other tasks.
Apart from providing market prices to users, ability to post bids and offers, e-marketplaces systems consist of a matchmaking feature to match user’s bids and offers for commodities. Providing such information to users contributes to improved negotiation power (e.g., farmers’ increase their power to negotiate with intermediaries, based on their ability to understand pricing in multiple markets); sophisticated marketing plans based on price information; access to better and variety markets; reduced logistics and transportation costs.