UAV technology and machine learning techniques applied to the yield improvement in precision agriculture

2017 ◽  
Author(s):  
Jaen Alberto Arroyo ◽  
Cecilia Gomez-Castaneda ◽  
Elias Ruiz ◽  
Enrique Munoz de Cote ◽  
Francisco Gavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Yield Improvement ◽  
Learning Techniques

scholarly journals Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

2020 ◽  
Vol 167 (3) ◽  
pp. 037522 ◽  
Author(s):  
Yemeserach Mekonnen ◽  
Srikanth Namuduri ◽  
Lamar Burton ◽  
Arif Sarwat ◽  
Shekhar Bhansali
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques

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 Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

2019 ◽  
Vol 11 (23) ◽  
pp. 2873 ◽  
Author(s):  
Ahmed Kayad ◽  
Marco Sozzi ◽  
Simone Gatto ◽  
Francesco Marinello ◽  
Francesco Pirotti
Keyword(s):  
Machine Learning ◽  
Grain Yield ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Corn Yield ◽  
Learning Techniques ◽  
Field Variability ◽  
Sentinel 2 ◽  
Corn Grain ◽  
Corn Grain Yield

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

scholarly journals Design and Development of IOT Based Intelligent Agriculture Management System in Greenhouse Environment

2019 ◽  
Vol 8 (5S3) ◽  
pp. 47-52
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Precision Agriculture ◽  
Stochastic Gradient ◽  
Machine Learning Techniques ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Learning Techniques ◽  
Time Calibration ◽  
Boosted Tree

The introduction of Internet of Things (IoT) has a significant impact on shaping the communication and internetworking landscapes. The upcoming IoT researches are linked with design of standards and open architectures still requiring a global attention before deployment. The main objective is to design and develop a framework on Internet of Things (IoT) for precision agriculture using Machine learning techniques, where it surges the efficiency in farming by minimizing the loss of water and studying the fertility of the field. Libelium Smart Agriculture is used to connect to the IoT which uses Waspmote module. Waspmote is the plug and sense platform which is programmed using Waspmote IDE configured to connect with the available Local Area Network (LAN). With the help of Machine learning techniques like Classification And Regression Technique (CART) and Linear Support Vector Machine (SVM), the amount of water required by the crops can be estimated. In this paper, various regression such as stochastic gradient decent and boosted tree regression techniques are compared and results were obtained. Although each model applied in this paper performed well in predicting whether the crop needs to be irrigated, the optimal prediction accuracies were acquired by Boosted Tree Regression (BTC). It is compared by the fold numbers, Root Mean Squared Error (RMSE) and coefficient of Determination (CoD). The accuracy of the boosted tree regression came out to be 91.93% and the stochastic gradient descent prediction model delivered 62.95% accuracy. The amount of water required for the irrigation is then sent to appropriate actuator like solenoid valve and motor can be turned on for that particular period of time. Calibration test results and Measurements are represented to enhance the accuracy and success rates of Precision Agriculture (PA).

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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