To What Extent Can Farmers Forecast The Growth Of Plants?

The growing global demand for food and the need for sustainable farming in an era of changing climate and scarce resources have led to significant crop yield prediction research. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. This work aims to develop and apply a deep network-based approach to predict plant growth by generating segmentation masks of root and shoot systems into the future.

Climate change is the biggest challenge to global food security, and protected cultivation can protect crops from extreme weather conditions and reduce the risk of extreme weather. Researchers from Skoltech have developed an AI-based approach to predict plant growth, which can help farmers make informed decisions about what to grow and when to grow.

Predicting plant growth and development is crucial for both farmers and horticulturists, as it can help solve issues such as long cycle, low efficiency, and uncertainty in plant breeding. Image-based crop growth modeling can contribute to precision agriculture by revealing spatial crop development over time.

Various approaches to crop yield prediction exist, including high-throughput phenotyping platforms (HTPP) and algorithms that interpret crop growth rate response to environmental and solar energy factors. These models can help growers improve environmental control for better production, match supply and market, and address the challenges of long cycle, low efficiency, and uncertainty in plant breeding.

In conclusion, understanding the growth and maturity rates of plants through air temperature measurement is essential for improving agricultural practices and ensuring food security.

What is the most accurate way to measure plant growth?

An auxanometer is a device utilized to quantify plant growth through the assessment of various morphological characteristics, including height, leaf size, flowering duration, and the presence and development of reproductive structures. The device records the increase in leaves and phenotypic indicators, with dry weight being the optimal method for accurate measurement.

What are the advantages of plant growth analysis?

Plant growth analysis is a method used to summarize and dissect changes in plant size over time. It is commonly used to compare different treatments, genotypes, or species. The simplest type of growth analysis evaluates plant size after a certain period of growth, typically from germination. In plant biology, size is often measured as the dry mass of whole plants (M). In high-throughput phenotyping platforms, the amount of green pixels from photographs taken from plants from various directions is often used to estimate plant size. If plant size was determined multiple times, the Absolute Growth Rate (AGR) is used to determine the increase in size over a given time period.

How do you predict crop production?

Climate change challenges are driving a revolution in harvest predictability, with methods such as environmental conditions, crop-growth models, satellite monitoring, meteorological statistical models, remote sensing technologies, artificial intelligence, and machine learning models being used. Accuracy is crucial in yield predictability, and a comprehensive yield intelligence bundle can complement humans and algorithms.

Traditional methods of crop yield predictability were once sufficient, but today’s landscape is more complex, with seasonal shifts no longer accounting for variations in accuracy. Combination methods are being explored to improve yield predictability and address the challenges faced by agriculture.

How do scientists measure plant growth?

An auxanometer is a device utilized to quantify plant growth. It accomplishes this by measuring the height, size, and number of leaves, comparing the results to those of fresh and dried plants, and evaluating the growth rate.

What algorithm is used for crop prediction?

Machine learning algorithms like regression, decision trees, and artificial neural networks are used for crop yield prediction. Regression algorithms are simple and easy to implement, using inputs like weather data, soil data, and management practices to predict crop yield. Decision tree algorithms use a tree-like structure to model decisions and their potential consequences, starting with the most important input factor and continuing with subsequent inputs.

Artificial neural networks, modeled after the human brain’s structure and function, are particularly well-suited for crop yield prediction due to their ability to handle large amounts of data and identify complex patterns and relationships.

Can we predict plant growth?

Artificial Intelligence (AI) has been used to analyze and predict plant growth performance, particularly in relation to environmental conditions, particularly ambient temperature. Climate change poses a significant challenge to global food security, and protected cultivation can protect crops from extreme weather conditions, reduce pests and diseases, and ensure food availability year-round. Globally, greenhouse environments are the most popular method for producing horticultural crops, with China becoming the world’s largest economy in protected horticulture. Chinese solar greenhouses (CSGs) are the second most popular greenhouse structure in China.

CSGs employ a passive thermal recycling system to reduce energy consumption through active heating, and external meteorological factors determine the internal microclimate. However, they have significant structural differences compared to greenhouses in the Netherlands, Israel, and Spain. CSGs have three thermal storage walls along their north, east, and west sides, with the north wall playing a crucial role in thermal storage, heat preservation, and insulation.

During the day, the greenhouse captures heat from the sun, storing it within the thermal mass of the walls, which is then released as a passive heating source at night. An insulating sheet closes over the transparent plastic sheet to reduce heat loss during the night. This passive solar heating strategy enables significant energy savings compared to the heating required to heat a glass greenhouse. A study in Manitoba, Canada, showed that the supplemental energy required to maintain temperatures above 10 degrees Celsius was 43 times less for CSGs compared to a glass greenhouse.

What are the advantages of crop prediction?

The study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato. It reviews publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF). Remote sensing, particularly satellite-based RS, is crucial for potato yield prediction and decision support over large farm areas.

CGM is often used to optimize management measures and address climate change. Currently, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) shows superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes.

The spatial approach has gained interest in recent years as it exploits field heterogeneity to manage over- or under-application of agronomical inputs, leading to higher yields and more environmentally friendly fertilizing schemes. Spatial interpolation methods, such as kriging, have been used to handle issues concerning spatial crop data. Block kriging and log10 transformation were compared for their effectiveness in reducing relative variance and estimating mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants after removing the “edge or margin” ones.

The results showed that log10 transformation reduced the coefficient of variance (CV) of all harvested original fresh weight (FW) plant data in the three plots from 35. 6–41. 6 to 6. 0–7. 5, while the respective CVs due to block kriging were reduced to 14. 5–19. 9.

Which algorithm is best for prediction?

Predictive analytics is a powerful tool that helps organizations predict future events and behaviors using old data. It involves various models and algorithms, each designed for a specific purpose. The power of predictive analytics lies in its ability to predict outcomes and trends before they happen. Organizations can use this knowledge to better understand their customers and businesses. The top 10 predictive analytics algorithms include Random Forest, Generalized Linear Model for Two Values, Gradient Boosted Model, K-Means, Prophet, Auto-Regressive Integrated Moving Average (ARIMA), LSTM Recurrent Neural Network, and Convolution Neural Network (CNN/ConvNet). Identifying the best predictive analytics model for a business is crucial for its strategy, as it can help reduce customer churn or analyze patient behavior after medical operations.

Which is the most reliable parameter to measure growth of the plants?

Dry weight is a reliable measure of plant growth due to its high water content and the challenging control over the amount of water in the environment. As a result, it is a more accurate indicator of plant health than other measures.

Can AI predict crop yield?

AI-powered crop yield prediction solutions are a valuable tool for agricultural companies to enhance crop yield accuracy and make informed decisions about crop management. These solutions are user-friendly and scalable, and can predict crop yield for various crops. They are also being used in wildfire detection and monitoring in government and interactive geospatial mapping for crime prevention. To discuss a project or workshop, schedule a demo call.

What are the advantages of crop simulation?

Crop modeling is a crucial tool for decision-making, forecasting crop growth and development, minimizing yield gaps, and selecting suitable genotypes and sowing dates for sustainable crop production in changing climatic scenarios. It helps in selecting suitable genotypes and sowing dates. Copyright © 2024 Elsevier B. V., its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.