Machine Learning in Agriculture_ Identifying Patterns to Improve Crop Performances - FarmERP

1. Machine Learning in Agriculture: Identifying Patterns to Improve Crop Performances - FarmERP A data-intensive approach is absolutely necessary for agribusinesses to stay relevant in today’s competitive landscape. Machine learning in agriculture can help in analysing large data sets and enable producers, field managers to make better decisions, improve crop performance and mitigate food safety concerns. With machine learning-based agricultural practices, making crop quality evaluations, yield estimates, disease predictions, etc becomes easier for agribusinesses. The global agriculture industry is at a critical juncture. According to the Food and Agriculture Organization of the United Nations, around 660 million people may be facing hunger in 2030. There is an urgent need to redefine our agricultural practices in order to meet the food demands of a rising population. The best way to do this is by assessing information and finding out patterns in every aspect of food production. Machine learning algorithms can assess numerous images, videos, data sets, and other farm information to identify trends in various agri functions. This method of data assessment and pattern recognition can be useful in selecting the right crop and obtaining accurate yield quality estimates. Problems related to crop and soil health can also be predicted and resolved well in advance with the help of machine learning in agriculture.

2. Here are some of the most valuable benefits of machine learning in agriculture – Picking the Most Suitable Crop for Cultivation To yield the most profitable outcomes for your farming efforts, it is important to make the right decisions from the first step itself. Choosing a crop based on the plot’s disease resistance, soil health, adaptation ability, as well as weather conditions can help your agribusiness meet its productivity goals. But how can you select a crop that’s perfectly suited to your farm? Machine learning in agriculture is the solution to this predicament. By providing machines with decades of data, agribusinesses can efficiently make accurate estimates about crop productivity. This can be particularly useful for agribusinesses that practice rotational farming with various seasonal crops. Machine learning in agriculture can thus identify the most lucrative crops and ensure high returns for farmers in all types of field conditions. Recognising Crop Yield Patterns With Agricultural Drones While the reliance on long-term information is necessary, it is never enough. Real-time data is also required to fine-tune your agricultural processes. Agricultural drones or unmanned aerial vehicles can be the best tools to gather real-time information of fields in the form of images and videos. Equipped with scanners, these drones can easily monitor large farmlands within minutes. Machine learning-based agriculture practices can further be helpful in combining the long-term and real-time databases to gain vital insights into the health of crops. With the information captured by drones, you can compare plant height, quality, diseases, and many other parameters with previous crop performances. This can help you draw out specific patterns and precisely predict the overall crop yield. Ensuring Adequate Water Usage and Sustainable Irrigation Water management in agriculture is one of the most important functions for agribusinesses to enhance productivity while also ensuring sustainability and cost-effectiveness. But for optimal water management, a well-defined and monitored irrigation system is necessary. Machine learning in agriculture can help producers and agribusinesses to efficiently carry out irrigation practices without any hassles. With the help of past data and machine learning algorithms, understanding the moisture requirements for a particular crop can become a lot easier. This can ensure the adequate utilisation of water resources and reduce farm expenses significantly. Similarly, the possibilities of crop damage due to over or under irrigation can also be completely prevented with machine learning-based agriculture.

3. Optimising Harvesting Functions With Agribots Labour wages account for a significant portion of farming expenses, especially in the case of plantations or large farmlands. These expenses shoot up even higher during peak harvest seasons when labour requirements are huge. Along with the number of labourers, a skilled and talented workforce is also necessary to prevent any errors during the harvesting process. Machine learning in agriculture can provide an efficient solution to this problem in the form of agribots. With machine learning empowered agribots, agribusinesses can accurately identify the right time to carry out harvesting operations. This not only enhances operational efficiency but also brings down labour costs and minimises post-harvest quality losses. Maintaining Good Health and Productivity of Farm Animals When it comes to livestock management, animal farming companies and farmers face huge challenges pertaining to infrastructure, connectivity, and the diet of the livestock. Keeping track of diseases and curbing their spread within the herd can also be quite difficult. With no effective solution to these concerns, animal farming companies can fail to safeguard the health of their livestock. Machine learning in agriculture can help you analyse numerous factors ranging from movement and diet changes to even pregnancy events of farm animals. It can make weight-gain predictions for a specific animal based on previous information. The integration of machine learning algorithms with regular medical observations can also assist in taking the right steps to make your farm animals more healthy and productive. The use of machine learning in agriculture has been instrumental in utilising data smartly to capture specific traits related to agricultural production and management. This is proving to be a great way for agribusinesses and enterprises to rethink their existing agricultural systems and move towards a more productive and secure future. Machine learning in agriculture can empower producers to enhance agricultural productivity while also minimizing its impact on the environment. FarmERP a farmer producer company software helps you to make your agricultural practices data-driven and grow crops more efficiently. Get in touch with us to know how our machine learning and artificial intelligence expertise can streamline your farming operations.