Plant Tech is the Future of Sustainability Initiatives
From seed to fork, every link in the food chain is beginning to change as a result of technological advancements. Farm operations are becoming more efficient and insight-driven in industrialised nations because to digital technologies and analytics. The five global trends driving agriculture's digital revolution are listed below. The next wave of agricultural revolution can be sparked by precision farming and technological developments in the supply chain, which can help address these issues and satisfy the growing global food demand. The fourth agricultural revolution is being shaped by these four key technological advances. In every industry, including agriculture, machine learning and sophisticated analytics are being utilised to mine data for trends.
Before planting seeds, these can be used with plant breeders. In order to provide farmers with the greatest breed for their location and environment, machine learning can forecast which traits and genes will be best for crop output. Machine learning methods used in the field can determine the difference between crops like maize and soy using satellite data, which is useful information for crop insurance, logistics, and commodity markets. This trend will be hastened even more by the convergence of robotics and data from an increasingly connected farm.
Digitization is causing a significant revolution in agriculture that makes it harder to predict market trends. The introduction of digital intervention has been seen in the agricultural industry. The majority of stakeholders are aware that digital innovation in the industry may help agriculture reach its next growth curve. It has the power to completely alter the flow of inputs, the cycle of crop management, and market access.
This edition of SI has introduced some society builders that have combined innovation and technology with agriculture to address some of the most pressing problems in the Indian agricultural sector. Let us know what you think.
Before planting seeds, these can be used with plant breeders. In order to provide farmers with the greatest breed for their location and environment, machine learning can forecast which traits and genes will be best for crop output. Machine learning methods used in the field can determine the difference between crops like maize and soy using satellite data, which is useful information for crop insurance, logistics, and commodity markets. This trend will be hastened even more by the convergence of robotics and data from an increasingly connected farm.
Digitization is causing a significant revolution in agriculture that makes it harder to predict market trends. The introduction of digital intervention has been seen in the agricultural industry. The majority of stakeholders are aware that digital innovation in the industry may help agriculture reach its next growth curve. It has the power to completely alter the flow of inputs, the cycle of crop management, and market access.
This edition of SI has introduced some society builders that have combined innovation and technology with agriculture to address some of the most pressing problems in the Indian agricultural sector. Let us know what you think.