Crop Yield Prediction Using Random Forest Algorithm

Suresh, N.; Ramesh, N. V. K.; Inthiyaz, Syed; Priya, P. Poorna; Nagasowmika, Kurra; Kumar, Kota.V.N.Harish; Shaik, Mashkoor; Reddy, B. Naresh Kumar

doi:10.1109/icaccs51430.2021.9441871

Cited by 32 publications

(4 citation statements)

References 3 publications

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“…To ensure the construction of a high-performance ensemble model, we utilized the APSIM alongside a range of diverse and reliable base learners, including multiple linear regression [62], LASSO regression [63,64], decision tree regression (DTR) [65], random forest (RF) [49,66,67], XGBoost [68], LightGBM [69], gradient boosting regression (GBR) [70,71], and extremely randomized trees regression (ERT) [72,73]. We optimized these eight machine learning algorithms through weighted ensemble techniques [74].…”

Section: Eight Machine Learning Algorithmsmentioning

confidence: 99%

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

Li,

Nie,

2024

Agronomy

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One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.

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Section: Eight Machine Learning Algorithmsmentioning

confidence: 99%

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

Li,

Nie,

2024

Agronomy

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“…The machine learning algorithm's performance evaluations when applied to the gathered field data show that, the estimated crop yield was less than 8% in 81% of test situations and the forecast error was less than 1.3%, which is crucial. Suresh et al [3] Proposed that in India's economy, agriculture is one sector that is essential. Inputs used in agriculture, including chemicals, fertilizers, insecticides, and soil condition.…”

Section: Related Workmentioning

confidence: 99%

Crop Yield Prediction Using Gradient Boosting Neural Network Regression Model

k¹,

Rao²,

Gunturu³

et al. 2023

IJRITCC

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The finest utility sector is agriculture, especially in emerging nations like India. Utilizing historical data in agriculture can change the context of decision-making and increase farmer productivity. Approximately a part of India's population is employed in agriculture, however this sector contributes just 14% of the country's GDP. This can be explained in part by farmers not making sufficient decisions on yield forecast. By examining numerous climatic elements, such as rainfall, and land characteristics, such as soil type and ground water salinity, as well as historical records of crops cultivated, the suggested machine learning technique tries to estimate the agricultural yield for a certain location. Finally, we anticipate that our proposed Machine Learning Gradient Boosting Neural Network Regression (Grow Net) model was predicting the accurate yield. Finally our system is expected to predict the yield based on dataset we have taken. We were compared our proposed algorithm with various Machine Learning algorithms such as Random Forest, Support Vector Machine, KNN, Multi-layer Perceptron Regressor, Gradient Boosting Regressor and results shows that proposed was given best RMSE ,MAE and R2 value.

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“…In [4] random forest algorithm is used for the prediction of crop yield. Predictions are done before the cultivation.…”

Section: Literature Reviewmentioning

confidence: 99%

Rice Leaf Disease Detection and Crop Yield Prediction Using Random Forest

M¹,

Anagha²,

Sifa³

et al. 2022

IJERCSE

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Rice production contributes a considerable amount to national income. Rice production can be affected by various diseases like brown spots, bacterial leaf blight, leaf smut caused by fungi, bacteria, etc. In this research, the diagnosis of rice plant leaf disease is done using random-forest classification and Digital image processing. The random forest classifier is efficient and accurate on a large dataset. The image is uploaded to the system by following digital image processing steps and using a random forest algorithm to perform on the processed image which outputs disease name, cause, symptoms, and remedy respectively. The proposed method also predicts the crop yield based on temperature, rainfall, humidity, and soil pH level. Overall, the model achieves 90% of accuracy

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Crop Yield Prediction Using Random Forest Algorithm

Cited by 32 publications

References 3 publications

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

Crop Yield Prediction Using Gradient Boosting Neural Network Regression Model

Rice Leaf Disease Detection and Crop Yield Prediction Using Random Forest

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