Data-driven, early-season forecasts of block sugarcane yield for precision agriculture

2022 ◽  
Vol 276 ◽  
pp. 108360
Author(s):  
Si Yang Han ◽  
Thomas Bishop ◽  
Patrick Filippi
Keyword(s):  
Precision Agriculture ◽  
Data Driven ◽  
Early Season ◽  
Sugarcane Yield

Data Driven Analysis of Locomotion for a Class of Articulated Mobile Robots

2021 ◽  
pp. 1-15
Author(s):  
Luca Carbonari ◽  
Andrea Botta ◽  
Paride Cavallone ◽  
Luigi Tagliavini ◽  
Giuseppe Quaglia
Keyword(s):  
Mobile Robots ◽  
Dynamic Model ◽  
Precision Agriculture ◽  
Autonomous Vehicles ◽  
Industrial Applications ◽  
Data Driven ◽  
Analysis Tool ◽  
Recent Past ◽  
Personal Assistance ◽  
Articulated Structures

Abstract In the recent past, the use of autonomous vehicles is becoming of relevant interest in several fields of application. Personal assistance, precision agriculture, and rescue are just few examples alongside the more common industrial applications. In many cases, the use of articulated structures is preferred to single chassis robots for their peculiar modularity. Moreover, they can be easily provided with locomotion units particularly suitable to overpass obstacles and to move on uneven grounds. Such vehicles are often built as an active front module and a rear one that is pulled passively or that can contribute to the vehicle traction when required. Understanding whether this contribution is convenient or not, it is the main matter of this paper. Two different mobile robots of different scale and purpose are taken into consideration. A dynamic model is presented and experimentally validated to be used as an analysis tool. At last, a simple yet effective actuation law is tested to evaluate the whether the contribution of the back module is beneficial or not to the whole machine manoeuvrability.

scholarly journals Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 232
Author(s):  
Tatiana Fernanda Canata ◽  
Marcelo Chan Fu Wei ◽  
Leonardo Felipe Maldaner ◽  
José Paulo Molin
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Mean Squared Error ◽  
Machine Learning Technique ◽  
Yield Mapping ◽  
Essential Information ◽  
Spectral Bands ◽  
Learning Technique ◽  
Sugarcane Yield ◽  
Imagery Data

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

scholarly journals Crop Recommendation using Machine Learning Techniques

2019 ◽  
Vol 9 (2S) ◽  
pp. 658-661
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Climatic Condition ◽  
Recommendation System ◽  
Soil Characteristics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Traditional Farming ◽  
Learning Techniques ◽  
The Right

Precision agriculture (PA) allows precise utilization of inputs like seed, water, pesticides, and fertilizers at the right time to the crop for maximizing productivity, quality and yields. By deploying sensors and mapping fields, farmers can understand their field in a better way conserve the resources being used and reduce adverse affects on the environment. Most of the Indian farmers practice traditional farming patterns to decide crop to be cultivated in a field. However, the farmers do not perceive crop yield is interdependent on soil characteristics and climatic condition. Thus this paper proposes a crop recommendation system which helps farmers to decide the right crop to sow in their field. Machine learning techniques provide efficient framework for data-driven decision making. This paper provides a review on set of machine learning techniques to support the farmers in making decision about right crop to grow depending on their field’s prominent attributes.

scholarly journals Precision Agriculture GIS Technologies for Mississippi, 1st. Edition

2019 ◽  
Author(s):  
Amelia A.A. Fox
Keyword(s):  
Precision Agriculture ◽  
Data Driven ◽  
Gis Technologies ◽  
Proper Management ◽  
Farm Efficiency ◽  
On Farm

Precision agriculture is meant to improve on-farm efficiency in hopes of ultimately increasing profitability while also protecting the environment. However, this difficult process almost always includes the proper management and interpretation of data. Therefore, it is imperative that those individuals involved in making such decisions are educated on these processes. In a data-driven world, this textbook is a great resource for those wanting to learn how to utilize their data in hopes of making better informed on-farm decisions.

scholarly journals The Precision Agriculture Using Artificial Intelligence

2021 ◽  
Vol 9 (11) ◽  
pp. 342-345
Author(s):  
Dr. C. K. Gomathy
Keyword(s):  
Artificial Intelligence ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Recurrent Neural Networks ◽  
Soil Management ◽  
Data Driven ◽  
Soil Parameters ◽  
Analysis Model ◽  
Hybrid Neural Network ◽  
Technological Developments

Abstract: Agriculture has been the sector of paramount importance as it feeds the country's population along with contributing to the GDP. Crop yield varies with a combination of factors including soil properties, climate, elevation and irrigation technique. Technological developments have fallen short in estimating the yield based on this joint dependence of the said factors. Hence, in this project a data-driven model that learns by historic soil as well as rainfall data to analyse and predict crop yield over seasons in several districts, has been developed. For this study, a particular crop, Rice, is considered. The designed hybrid neural network model identifies optimal combinations of soil parameters and blends it with the rainfall pattern in a selected region to evolve the expected crop yield. The backbone for the predictive analysis model with respect to the rainfall is based on the TimeSeries approach in Supervised Learning. The technology used for the final prediction of the crop yield is again a branch of Machine Learning, known as Recurrent Neural Networks. With two inter-communicating data-driven models working at the backend, the final predictions obtained were successful in depicting the interdependence between soil parameters for yield and weather attributes. Keywords: Precision agriculture, Artificial intelligence, Crop management, Solutions, Yield, Soil management

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