Machine learning can be applied in agriculture to improve crop yield, reduce waste, and increase efficiency in farming operations. It can be used to analyze soil data, weather patterns, and other factors that affect crop growth to optimize farming practices.

Machine learning (ML) can be applied in agriculture in various ways to improve crop production, reduce costs, and increase efficiency. Here are some examples:

Overall, machine learning can help farmers make more informed decisions, reduce waste, and increase efficiency, leading to higher crop yields and more sustainable agricultural practices.

Dear Sudip Ghimire, in addition to the above answers provided by Ahshanul Haque, others can also include the following:

(1) Robots – Hyper-efficient AI harvesting bots can replace human workers in the agricultural sector and reduce labor costs. They can also help farmers protect their crops by tracking and spraying weeds.

(2) Watering – Farmers use AI to monitor growing areas for crop humidity, soil composition, and temperature. This AI use results in increased yields due to water and fertilizer use optimization.

(3) Agric Resource Management – AI can help farmers save energy, reduce pesticides, and shorten the time to market.

(4) Optimization of nitrogen in the soil – Nitrogen is a vital nutrient that allows plant growth. Although nitrogen is prevalent in the ground and the atmosphere, plants can only use a tiny percentage of the nitrogen in the soil. Farmers can keep these inorganic nitrogen levels at optimum levels with the help of machine learning technology.

(5) Nitrogen modeling predicts the nitrogen cycle in the atmosphere and the soil, thus guiding the farmer to optimum levels. Simulation software can check nitrogen availability and calculate when to add nitrogen to the soil. Conversely, it can also alert the farmer to too much nitrogen, which can poison the crops.

(6) Species breeding – Species selection is a painstaking task involving searching for specific genes to ensure effective responsiveness to water and nutrients. Ideal plant species will cope with climate change, be disease-resistant, have higher nutritional content, and taste better.

(6) Machine learning allows us to draw from decades of field data for detailed crop performance analysis. A probability model from this data predicts which genes will contribute a sought-after genetic advantage to a plant.

(7) Species recognition – Traditionally, plant classification has been done by basic comparisons such as the color and the shape of the leaves. Machine learning enables much more complex, accurate, and faster analysis of plants using more sophisticated techniques such as analyzing leaf vein morphology.

Although ML-driven farms are in their infancy, these few examples indicate that they are already evolving into factories run by machine learning. Currently, machine-learning solutions in agriculture tend to deal with individual problems.

AI systems are helping to improve the overall harvest quality and accuracy – known as precision agriculture. AI technology helps in detecting disease in plants, pests and poor nutrition of farms. AI sensors can detect and target weeds and then decide which herbicide to apply within the region. ML-based deep learning can simplify the task of crop breeding. Algorithms simply collect field data on plant behavior and use that data to develop a probabilistic model. Crop yield prediction is another instance of machine learning in the agriculture sector. Today's farmers rely upon ML-enhanced technology to: Map and estimate yields and better meet demand without unnecessary waste and to make smarter harvesting and pricing decisions through identify and automatically remove harmful weeds and find and treat crop disease with targeted solutions which accurately classify weed species. These farmers are receiving support in the form of various AI technologies, including sowing quality testing, soil testing, crop health monitoring, window prediction and tillage estimation, as well as accessing new customers and suppliers in different geographies.