High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method
TLDR
The Wondercane model is an accurate and robust tool that can substantially reduce the issue of sugarcane yield estimate errors and provide the sugar industry with improved pre-harvest assessment of sugarCane yield.Abstract:
This article presents a new model for forecasting the sugarcane yield that substantially reduces current rates of assessment errors, providing a more reliable pre-harvest assessment tool for sugarcane production. This model, called the Wondercane model, integrates various environmental data obtained from sugar mill surveys and government agencies with the analysis of aerial images of sugarcane fields obtained with drones. The drone images enable the calculation of the proportion of unusable sugarcane (the defect rate) in the field. Defective cane can result from adverse weather or other cultivation issues. The Wondercane model is developed on the principle of determining the yield not through data in regression form but rather through data in classification form. The Reverse Design method and the Similarity Relationship method are applied for feature extraction of the input factors and the target outputs. The model utilizes data mining to recognize and classify the dataset from the sugarcane field. Results show that the optimal performance of the model is achieved when: (1) the number of Input Factors is five, (2) the number of Target Outputs is 32, and (3) the Random Forest algorithm is used. The model recognized the 2019 training data with an accuracy of 98.21%, and then it correctly forecast the yield of the 2019 test data with an accuracy of 89.58% (10.42% error) when compared to the actual yield. The Wondercane model correctly forecast the harvest yield of a 2020 dataset with an accuracy of 98.69% (1.31% error). The Wondercane model is therefore an accurate and robust tool that can substantially reduce the issue of sugarcane yield estimate errors and provide the sugar industry with improved pre-harvest assessment of sugarcane yield.read more
Citations
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Journal ArticleDOI
Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits
M. R. Meena,Chinnaswamy Appunu,R. Arun Kumar,R. Manimekalai,S. Vasantha,Gopalareddy Krishnappa,Ravinder Kumar,S. K. Pandey,G. Hemaprabha +8 more
TL;DR: This review will focus on the recent advances in Sugarcane genomics related to genetic gain and the identification of favorable alleles for superior agronomic traits for further utilization in sugarcane breeding programs.
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Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning
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TL;DR: In this paper , the authors proposed to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors.
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Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane
Karansher S. Sandhu,A. Shiv,Gurleen Kaur,M. R. Meena,Arunkumar Raja,Krishnapriya Vengavasi,A. K. Mall,Praveen Singh,J. Singh,G. Hemaprabha,A. D. Pathak,Gopalareddy Krishnappa,Sanjeev Kumar +12 more
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The use of UAS-based high throughput phenotyping (HTP) to assess sugarcane yield
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