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Predict And Build Crop Forecasting Model Using Artificial Intelligence:
Predict And Build Crop Forecasting Model Using Artificial Intelligence:

August 3, 2021

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Crop forecasting is the art of predicting crop yields and production before the harvest actually takes place, typically a couple of months in advance.  They need, therefore, information and data on the most important factors that affect crop yields - the model inputs. Crop forecasting relies on computer programs that describe the plant-environment interactions in quantitative terms. Such programs are called "models", and they attempt to simulate plant-weather-soil interactions. After passing "through" the model, the inputs are converted to a number of outputs, such as maps of crop conditions and yields.

Mainly Two types of models 1) Statistical Models

                                             2)Crop Simulation Models

Statistical Models:

Time is one of most important factors on which our businesses and real life depends. But, technology has helped us manage the time with continuous innovations taking place in all aspects of our lives. Don’t worry, we are not talking about anything which doesn’t exist. Let’s be realistic here.

Here, we are talking about the techniques of predicting & forecasting future strategies. The method we generally use, which deals with time-based data that is nothing but “Time Series Data” & the models we build ip for that is “Time Series Modeling”. As the name indicates, it’s basically working on time (years, days, hours, and minutes) based data, to explore hidden insights of the data and trying to understand the unpredictable nature of the market which we have been attempting to quantify.

Crop Simulation Models:

Agricultural production managers, natural resource managers, and strategic decision makers require accurate, timely, and cost-effective information to maintain a quality food and fiber supply for the nation and the world. The Crop Systems and Global Change Laboratory conducts research to develop crop simulators for predicting growth, development, and yield of agricultural crops.

Crop simulators are computer programs that mimic the growth and development of crops. Data on weather, soil, and crop management are processed to predict crop yield, maturity date, efficiency of fertilizers and other elements of crop production. The calculations in the crop models are based on the existing knowledge of the physics, physiology and ecology of crop responses to the environment.

Why Artificial Intelligence and Machine Learning is important to build crop forecasting model:

AI and Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. Here, the methods are analyzed and features has been investigated. According to analysis, the most used features are temperature, rainfall, and soil type and the most applied algorithm is Artificial Neural Networks in these models. In many cases, Electronic databases and Systematic Literature Review have been used to identify deep learning-based studies, synthesize the algorithms  and extracted the applied deep learning algorithms.

Methodology:

1.    Review the protocol:

The first stage is planning the review. In this stage, research questions are identified, a protocol is developed and the protocol is validated to see if the approach is feasible. In addition to the research questions, publication venues, initial search strings, and publication selection criteria are also defined. When all of this information is defined, the protocol is revised one more time to see if it represents a proper review protocol. 

2.    Conducting the review:

The second stage is conducting the review. When conducting the review, the publications were selected by going through all the databases. The data was extracted, which means that their information regarding authors, year of publication, type of publication, and more information regarding the research questions were stored. After all the necessary data was extracted correctly, the data was synthesized in order to provide an overview of the relevant papers published so far.

3.    Reporting the review:

The review was concluded by documenting the results and addressing the research questions.

4.    Data and data processing:

For example, in the 2018 Syngenta Crop Challenge, participants were asked to use real-world data to predict the performance of corn hybrids in 2017 in different locations. The dataset included 2,267 experimental hybrids planted in 2,247 of locations between 2008 and 2016 across the United States and Canada.

The training data included three sets: crop genotype, yield performance, and environment (weather and soil). The genotype dataset contained genetic information for all experimental hybrids, each having 19,465 genetic markers.

The genotype data were coded in {−1, 0, 1} values, respectively representing ‘aa, aA, and AA’ alleles. Approximately 37% of the genotype data had missing values. To address this issue, they used a two-step approach to preprocess the genotype data before they can be used by the neural network model.

5.    Weather Prediction:

Weather prediction is an inevitable part of crop yield prediction, because weather plays an important role in yield prediction. For each weather variable w, the neural network model explains the weather variable at location l in year y as a response of previous years at the same location.

6.    Yield Prediction Using Deep Neural Networks:

 First, we have to train two deep neural networks, one for yield and the other for check yield and then used the difference of their outputs as the prediction for yield difference in an effective way.

 Use of Artificial Intelligence in Models:

1) A satellite-based solution to provide a real-time update of the crop progress, called In-Season Tracker. It uses field surveys, analytics, remote sensing, and Nat Cat (natural catastrophe) modelling.

2) Policy Verification tool that helps insurance companies in the verification of crop insurance policies using large-scale automation and machine learning.

3)The Yield and Acreage Outlook module generates estimates based on forecasted and actual weather conditions. This model uses a combination of AI, machine learning, along with meteorology and geospatial informatics to provide comprehensive pan-India data on crop yield and acreage estimation. It also helps in better understanding of potential agri-distress hotspots for early mitigation.

Conclusion:

Farmers are using PA to improve agricultural accuracy by creating probabilistic models for seasonal forecasting. Agricultural AI technologies can then optimize farm management by basing decisions on predicted weather patterns during the coming season.


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