Application of AI and Machine Learning For Crop Yield Estimation Times of Agriculture : e-Magazine

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Agriculture has significantly benefited from using technology in various areas like seed production, fertilizers, and pesticides. Biotechnology and genetic engineering have helped create pest-resistant plants, leading to higher crop yields. Mechanization has made tillage more efficient, reduced manual labour, and made harvesting less labour-intensive. Technological progress has also enabled the improvement of irrigation methods and transportation systems.

Technological development is focused on robotics, precision farming, artificial intelligence and blockchain technology. Advances in these industries are being used to shape new farming practices and increase crop yields. They also open lots of new opportunities for farmers and agronomists, including monitoring of crop harvesting process.

Crop Yield Estimation

Accurately predicting crop yields is vital for global food security, but it can be challenging due to various factors such as genotype, environment, and management, which interact in complex ways. Advanced predictive and interaction regression models were developed to address this issue. This model combines optimization, machine learning, and agronomic knowledge to improve yield predictions and has three essential properties.

This model has achieved a relative standard error of 8% or less to predict corn and soybean yields. It also revealed about a dozen environment-by-management interactions for corn and soybean yield, some consistent with traditional agronomic knowledge. In contrast, other interactions require additional analysis or experiments to prove or disprove. Third, it quantified crops by weather, soil, management, and interactions. With this information, agronomists could pinpoint the factors that favoured or adversely affected crops.

The new forecasting model has a significant. It can generate both accurate forecasts and clear explanations at the same time. It became possible thanks to training the algorithm to choose features and interactions consistent in space and time, enabling accurate predictions on training data and generalization to test data.

Satellite technologies make it possible to cover large areas of agricultural land and develop effective methods for assessing yields. A team of scientists and engineers from EOS Data Analytics, a satellite imagery analytics company, uses remote sensing and machine learning models to estimate yields accurately.

Based on Earth observation data obtained using remote sensing technologies, EOSDA specialists can predict the current season’s crops up to 3 months in advance. The estimation accuracy varies from 85% to 95% and depends on the quality of the statistical data.

In agriculture, predicting crop yields is challenging and has significant implications for global, regional, and local decision-making. Decision support models are commonly used to gather crucial crop data for yield forecasting.

Precision agriculture, which involves monitoring via sensor technology, management information systems, and variable application technologies, aims to respond to both external and internal farming system variability.

The advantages of precision farming include increased crop yields and quality, with a reduced environmental impact. Yield modelling is a valuable tool for comprehending how water and nutrient shortages, pests, diseases, yield inconsistencies, and other field conditions can impact crop yield over time.

Use of AI in Crop Science

The practical application of AI tools can be complex for farmers to understand and is often perceived as something that can only be applied in the digital world. However, this technology can help cultivate physical land. The challenge for agrotech providers is clearly explaining why their solutions are helpful and how they should be implemented. AI solution providers have a lot of work to do to help farmers properly implement and apply these tools.

AI tools allow you to quickly and efficiently analyse and classify a large amount of data. In this way, information stored in gene banks can be processed. In addition, the harvesting process can be significantly accelerated through robotic sensors, remote sensing images and drones.

Scientists can use AI tools for the identification of t desirable traits, including tolerance to drought, heat, and various diseases, which can be used to breed more resilient crops needed to overcome the effects of climate change.

Prediction technologies can also forecast local climate conditions in a particular region for several years ahead. For example, it can help discover increasing temperatures or precipitation and how these changes affect local crops.

Using Machine Learning for Crop Yield Predictions

Machine learning enables computers to learn from data without being explicitly programmed, making it ideal for yield prediction. This technology can identify patterns and relationships in large amounts of data and make predictions based on them.

Various types of machine learning algorithms can be used to predict yields, including regression, decision trees, and artificial neural networks.

Regression algorithms are commonly used to predict yields. They are easy to understand and implement using a set of inputs, including weather data, soil data, and management practices, to predict an outcome.

Decision tree algorithms application is one more way to predict yields. They use a tree structure to model decisions and their possible consequences.

Artificial neural networks are more complex algorithms. They are modelled similarly to the human brain’s structure and function. These algorithms can process large amounts of data and reveal intricate patterns and connections. Artificial neural networks can also be used for yield prediction.

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