Agriculture Sector is the backbone of an economy and it also satisfies basic human needs. In addition, most of the countries consider agriculture the major source of employment. Due to the continuous increment in the population day, there is a hiring burden on the agriculture sector. Implementing Machine learning practices in agricultural systems can help in tacking with these issues. Furthermore, the machine learning application in agriculture relies on real-time data and helps in delivering exponential gains for farmers. Enrolling in the Machine Learning Course can help you contribute to the Machine Learning approach towards farming. Here are some top applications of machine learning in the farming sector.
Crop management
It is a pre-harvesting activity that plays a significant role in future yields. It is a challenging and unpredictable stage that can be ruined by issues such as drought, higher temperatures, and unpredictable wetting. Etc. Therefore, implementing Machine Learning can help in amplifying this stage. It can simplify the tasks of crop breeding by collecting and analyzing the necessary data and then developing a probabilistic model.
Precision spraying
Spraying plays a very important role in crop health by preventing pest & disease infestation. Implementing Machine Learning in the Agriculture sector can help in managing this area as well. It facilitates technologies and processes such as precision spraying which can be done through intelligent software and computer vision. Above all, this technique ensures a much more precise and accurate application of pesticides and fertilizers as per the crop requirements.
Insect detection
Insects are one of the main threats that farmers face and they are responsible for 20 % of global production loss. Recently there has been a new approach to use drones to detect insects in farming. Machine learning companies help farmers label pests to capture and identify them. For this, data engineers use real-time images of insects to accurately identify each species with more than 90 % accuracy.
Field conditions management
Soil and water management is one of the most important aspects of farming and managing it can be hard. This technology helps in collecting the soil & water-related conditions and provides recommendations for fertilizer application rates, pest control options, and irrigation schedules. Along with this, it calibrates soil sensors which helps in protecting the deficient nutrient and water stress.
Automatic weeding
Machine Learning also assists in processes regarding weed controls. A few types of robots have been developed that can target individual weeds in the crops and eliminate broad-leaved weeds using pattern recognition. Furthermore, this approach results in enhancing the yields at the individual plant level. These robots come with lasers that can burn out the weeds at the point of growth and prevent them from further growth.
Automatic harvesting robots
Numerous machine Learning companies are continuously working towards building intelligent tools to automate the harvesting of agricultural produce. ML helps robot systems with more accuracy and assists in picking the necessary fruits or vegetables. These robots can identify and harvest while maintaining their quality. Above all, this approach results in minimizing the harvesting time and increases profit margins for farmers.
Conclusion
Implementing machine learning in Agriculture is one of the fields with the quickest growth rates. This technology can be useful in farming and it results in obtaining great fruitful results. It starts with obtaining trustworthy input data for the data analysis and then suggesting beneficial methods and approaches as per the analysis reports. Enrolling in the Machine Learning Using Python can surely help you start a career in this domain. In conclusion, implementing machine learning in farming & agriculture results in effective decision-making along with increased efficiency, and decreased waste.
