International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Rice Leaf Disease Detection and Crop Yield Prediction Using Random Forest

Author : Shilpa Ajeesh M ¹ Anagha C ² Fathima Sifa ³ Hilma T ⁴ Deepthi P ⁵ Jijina M T ⁶

Date of Publication :1st August 2022

Abstract: 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.

Reference :

1. Wahyuni Eka Sari, Yulia Ery Kurniawati, Paulus Insap Santosa, “Papaya Disease Detection Using Fuzzy Na¨Ä±ve Bayes Classifier”, 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 2020, doi: 10.1109/ISRITI51436.2020.9315497.
2. Tong Xiao, Heyun Liu, Yun Cheng, ”Corn Disease Identification Based on improved GBDT Method”,2019 6th International Conference on Information Science and Control Engineering (ICISCE), 215-219, 2019,doi.org/10.1109/ICISCE48695.2019.00051.
3. Bhavini J. Samajpati,Sheshang D. Degadwala, ”Hybrid approach for apple fruit diseases detection and classification using random forest classifier”,2016 International Conference on Communication and Signal Processing (ICCSP), IEEE,
4. Namgiri Suresh, N.V.K.Ramesh, Syed Inthiyaz, “Crop Yield Prediction Using Random Forest Algorithm”. 2021 7th International Conference on Advanced Computing Communication Systems (ICACCS).doi: 10.1109/ICACCS51430.2021.9441871.
5. Archana, K. S., Sahayadhas, A. (2018). Automatic rice leaf disease segmentation using image processing techniques. Int. J. Eng. Technol, 7(3.27), 182-185.
6. Bosch, A., Zisserman, A., Munoz, X. (2007, October). Image classification using random forests and ferns. In 2007 IEEE 11th international conference on computer vision (pp. 1-8). Ieee.
7. Lu, D., Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International journal o f Remote sensing, 28(5), 823-870.
8. S.Veenadhari, Dr Bharat Misra, Dr CD Singh.2019.”Machine learning approach for forecasting crop yield based on climatic parameters.”. International Conference on Computer Communication and Informatics (ICCCI), 2014.