The Implementation of Machine Learning and Deep Learning Algorithms for Crop Yield Prediction in Agriculture
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AGRI ARTIC 2nd Rahimov
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1. Related works
In recent years, there has been significant research interest and activity focused on the topic of crop yield prediction. Numerous studies have been conducted in this area, exploring various techniques and methodologies for predicting crop yields with greater accuracy and precision. Koirala et al. (2019) reviewed the use of Deep Learning methods for fruit counting and estimating yield. They revealed the ability of Deep Learning methods to extract important features while recommending approaches such as CNN detectors, deep regression, and LSTM for estimating the fruit load [3]. Dharani et al. (2021) conducted a review on crop yield prediction using Deep Learning and found that hybrid networks and RNN-LSTM networks outperformed other networks. The superior performance of RNN and LSTM can be attributed to their storage and feedback loop capabilities, enabling them to make accurate predictions with time-series data on crop yield [4]. In their study on crop yield prediction using Machine Learning, van Klompenburg et al. (2020) found that neural networks, specifically CNN, LSTM, and DNN, were the most commonly used models. They also noted that the number of features used varied depending on the study and that in some cases, yield prediction relied on object counting and detection instead of tabular data [5]. Amit et al. proposed their model that predicts winter crop yield of wheat using DNN, convolutional neural network(CNN) and XGboost. Their proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data) [2]. |
