We recently explored the new concept of smart farming, which uses high-precision algorithms to make agriculture more efficient and effective. Machine Learning crash course — the scientific field that provides robots the ability to learn without being explicitly programmed — is the mechanism that drives it. It has arisen with big data technology and high-performance computers to open up new avenues for unraveling, quantifying, and comprehending data-intensive processes in agricultural operational environments.
Breeding of Species
This application is our favorite because it is both sensible and unexpected. After all, you usually hear about harvest prediction or ambient conditions management at a later stage.
Species selection is a time-consuming process of looking for specific genes that determine water and nutrient usage efficiency, adaptation to climate change, disease resistance, nutrient content, and taste. Deep learning algorithms, in particular, use decades of field data to examine crop performance in varied climates and novel features developed in the process. Based on this information, they may create a probability model that predicts which genes are most likely to give a positive characteristic to a plant.
While the typical human approach to plant classification is to compare leaf color and shape, machine learning can deliver more accurate and faster answers by studying leaf vein architecture, which contains more information about the leaf attributes.
Management of field conditions
The soil is a diverse natural resource with complex processes and hazy mechanisms for agricultural scientists. Its temperature alone can provide information on the implications of climate change on the regional output. Machine learning algorithms investigate evaporation processes, soil moisture, and temperature to better understand ecosystem dynamics and agricultural impacts.
Agriculture’s water management impacts hydrological, climatological, and agronomic balance. So far, the most advanced ML-based applications are linked with an estimation of daily, weekly, or monthly evapotranspiration, allowing for more efficient use of irrigation systems and prediction of daily dew point temperature, which aids in identifying common weather phenomena and estimating evapotranspiration and evaporation.
Prediction of Yield
Yield prediction is one of the most critical topics in precision agriculture since it determines yield mapping, estimating crop supply and demand matching, and crop management. Modern systems go well beyond simple prediction based on past data, incorporating computer vision technologies to offer data on the go and comprehensive multidimensional analysis of crops, weather, and economic situations to maximize production for farmers and the public.
Accurate identification and classification of crop quality attributes can raise product prices while decreasing waste. Compared to human specialists, machines can employ seemingly useless data and linkages to disclose and discover new features that play a role in the overall quality of crops.
The most extensively employed pest and disease control practice in both open-air and greenhouse situations is to uniformly spray insecticides over the cropping area. To be effective, this strategy necessitates the use of large volumes of pesticides, which comes with a tremendous financial and environmental cost. This was in a nutshell about Machine learning in agriculture. To know more about Data Science Post Graduate courses, click here.