Smart Farming is a one-stop data-driven solution to address the various challenges faced by farmers at different stages of their operations. The main objectives of smart agriculture system are to increase productivity and business profit through smart farming technologies .
Stages of Farming
The entire cycle of farming can be divided into Pre-harvest and Post-harvest,
Pre-Harvest Activity:
Soil quality, seed quality, irrigation methods, labour analytics.
Soil quality: the most important thing in agriculture is the soil. There are factors that determine the quality of the soil and can be measured by various indicators, e.g. physical (air permeability, clay mineralogy, oxygen diffusion rate, soil strength, water holding capacity, etc.), chemical (pH, GNP, plant nutrient availability, etc.). Biological (soil respiration, biomass C/total organic matter, nucleic acid analysis, substrate utilization).
In traditional agriculture, assessing soil quality is quite a challenge. This is where smart agriculture system comes in, and the innovative approaches of digital soil surveying, soil modelling, kriging, machine learning, remote sensing, soil mapping, soil science, and digital elevation metrics help predict the fundamental properties of soil and enable better decisions.
Fertiliser recommendation: advising farmers on the appropriate amount of fertiliser to use, taking into account a variety of factors such as soil type, crop characteristics, and climate zone,
Seed quality :
Seed is the basis for any rice plant. It should be carefully developed, harvested and handled for best yield and quality results. Sowing high quality seed results in lower seeding rates, better development, greater consistency, less replanting, and enthusiastic early development that increases protection against bugs and infection and prevents weeds. The result is higher yields
In traditional agriculture, selecting the right seed for the right sand is not an easy task due to a lack of data and techniques.
In smart agriculture, we can use advanced analytical techniques to measure seed physical purity, moisture content, viability, germination, seed vigour, etc., and seed health.
Weather information:
The most unpredictable factor affecting agriculture is the weather. If the weather permits, the outcome will be as planned. If not, the entire effort and process will collapse, which will be the company’s undoing. Will it be predicted?
Over the past decade, Artificial Intelligence (AI) has been gradually gaining ground in barometric science. It takes climate information and constructs connections between the accessible information and the corresponding indicators. ML can help work on truly sound models, and by combining the two methods, they can produce accurate results. Complex models and ML are used to assess weather conditions using a mix of current models and estimated information at PC.
Irrigation Methods :
Intelligent irrigation management based on Internet of farming Using sensors connected to the network to monitor and manage water can reduce water waste, maintain crop health, and improve yield. Farmers could use and analyse data from the sensors to manage irrigation to meet demand while conserving natural resources. Over time, sustainable smart farming strategies can increase revenue while saving water.
Workforce analytics: wages, employees and time management :
Properly scheduling when to do something and for whom makes every job successful from the ground up. With the power of AI, a farmer can predict and track required labour, budget, and process duration.
With these activities and processes, a farmer has the greatest chance of getting the product they want. Now is the time to address the post-harvest areas that are the answer to every farmer’s marketing and profit questions, such as:
Where to sell?
The role of AI in smart agriculture bridges the gap between supply and demand challenges, challenges and opportunities. We (hyperlink) provide the best solutions for your agriculture needs. Our team of experts listens to your requirements and is always available to help.
In smart agriculture, a farmer is able to see their potential market to sell their products, which was not possible in earlier times. With the help of Big Data, they can now analyse the competitive prices in the market, compare them with the MSP and also predict the future demand for their goods.
Transportation Analytics :
Now the farmer has found the right customer and closed his deal at the best price. With the help of AI, he can identify the best route and mode of transportation to deliver his goods to the customer. With the help of GPS and integrated IOT, this becomes a reality.
Conclusion :
The role of AI in agriculture bridges the gap between supply and demand challenges and opportunities, providing the data needed to take the labour process to the next level. Needless to say, it’s a game changer in terms of efficiency, productivity, supply chain and ultimately profit improvement. We provide the best solutions for your agribusiness needs. Our team of experts listens to your requirements and is always available to advise you.
