How is machine learning used in agriculture and how is future farming advancing agriculture with artificial intelligence?
Rk Naresh i will try to answer your question.
Machine learning is increasingly being used in agriculture to improve various aspects of farming practices, crop management, and decision-making processes.
Some common examples that i can collect while writing this answer is -
1 - Predictive analysis for crop yields- By utilizing the past data such as weather patterns, soil conditions, crop types, and agricultural practices to predict crop yields. (Here inside the clusters we need to add further clusters for being near to the actual results)
2- Disease Detection : Just like the imaging we are using in the medical for the disease detection in humans similar model can be made for plants as well. ML and Deep learning algorithms can analyze images of crops to detect signs of diseases, pests, or nutrient deficiencies. Early detection allows farmers to take proactive measures to prevent the spread of diseases and minimize crop losses.
3 - Precision Agriculture: ML enables precision agriculture techniques by analyzing data from sensors, drones, and satellite imagery to optimize resource allocation and improve crop productivity. For example, machine learning algorithms can recommend precise amounts of water, fertilizer, and pesticides based on real-time environmental conditions and crop requirements.
or maybe utilizing those trends a autonomous device can be built which can be helpful in reducing the dependency onto the labs.
In addition to the points that have been mentioned by mentioned by Dr Shivam, the future of ML in advancing farming revolves around preventive analytics. This involves predicting the potential diseases and issues that are likely to emanate at a later time based on the present conditions. This will empower farmers to take preemptive approaches to disease detection and outbreak rather than a reactive approach.
Early detection of problems will enable farmers to take preventive actions. This can involve targeted application of pesticides or fungicides, adjusting irrigation practices, or even selecting disease-resistant crop varieties. This proactive approach will minimise crop loss, improve yield, and reduce reliance on reactive measures.
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