Easy yield mapping for precision agriculture

The chapter deals with techniques used for yield monitoring and mapping in precision agriculture. Yield maps are one of the most valuable sources of spatial data for precision agriculture. In developing these maps, it is essential to remove the data points that do not accurately represent the yield at a corresponding location. Map averaging or smoothing is usually done to aid data interpretation. A long yield history is essential to avoid drawing conclusions that are affected by the weather or other unpredictable factors during a particular year. Typically, at least 5 years of yield maps are desired. Processed yield maps can be used to investigate factors affecting the yield or to prescribe variable rate applications of agricultural inputs according to spatially variable yield goals (yield potential). Producers interested in precision farming should, however, always evaluate different management approaches to identify those that provide the greatest benefit at a particular site.

An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

A study was set forth to determine if lower cost GPS receivers are suitable forprecision farming practices such as yield mapping. A combine equipped with a yield monitoringsystem was configured so that yield data could be collected simultaneously using a lower costGPS receiver and a DGPS receiver. Yield maps were then produced from the same field usingstandard yield mapping techniques. Comparisons were then made between the maps. Onecomparison made was analyzing the positioning differences between the two receivers. Resultsshowed average relative positioning differences of 1.78 meters. The other comparison madewas how similar the yield maps were at showing variability and yield. Visually one could not tellany difference between the maps. Using the yield maps to create management zones, similaritybetween the maps ranged from 66.4% to 79.7% of being the same.

Two mail surveys were carried out in Denmark and the Eastern Corn Belt, USA in 2002. Questionnaires were sent to 580 farmers who had used precision agriculture (PA) and 198 responses were received. The surveys focused on the current status of use of PA in both countries, including: PA practices, equipment and software, Internet and e-mail use, information sources for PA, satisfaction level from service providers, data handling, interpretation, storage and ownership, value of data for decision making, changes in management practices, desired information and services, and the next planned step in the practice of PA. The survey results showed more similarities in practicing PA between the two countries than differences. Time requirement and high cost of data handling were cited as the main problems. Survey respondents found soil maps to be more valuable than yield maps in management decisions. About 80% of the respondents would like to store the PA data themselves. The majority of the respondents indicated that they have changed their management practices due to PA, but not substantially. Some 90 of the respondents used the Internet and e-mail for agricultural purposes, but only a small number for PA websites.

Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

Geometry-aware PointNet for rapid prediction of cerebral aneurysm hemodynamics.

With increasing attention to urban temperature and outdoor thermal comfort, monitoring urban microenvironments at a lower cost is an effective method to supplement the spatiotemporal deficiencies of traditional monitoring networks. But widespread use of low-cost sensors has been hampered by uncertainty about their data quality. The calibration of low-cost sensors is key to promoting their large-scale application and increasing people's confidence in related research. The purpose of this study is to calibrate low-cost integrated environmental sensors and effectively improve their hourly data quality based on an IoT case study in Wuhan, China.Based on the standards of 24 traditional weather stations in different locations of the meteorological regulatory authorities, this study applied a total of eight machine learning (ML) algorithms to calibrate low-cost sensors and compared their performance. The eight ML algorithms are: (a) Multiple Linear Regression (MLR); (b) Random Forest (RF); (c) K-Nearest Neighbors (KNN); (d) Gradient Boosting Regression Tree (GBRT); (e) Decision Tree (DT); (f) AdaBoost; (g) Bagging; (h) Extremely randomized Trees (Extra-Trees). Hourly raw data collected by 34 low-cost sensors deployed near traditional weather stations were calibrated, and the model was tested using ten-fold cross-validation. The two farthest locations are 121km apart in a straight line, and the maximum data collected from a single sensor is 12,406 hours. In addition, the model migration effects in different field scenarios were also considered, including six typical land surface types, namely built area, scrub, water, artificial surfaces, woodland, and cultivated land.The results show that the random forest model shows better performance than other models on multiple low-cost sensors at different locations. By applying our method, it shows an average improvement with its R-squared value from 0.682 to 0.980, Root Mean Square Error (RMSE) from 5.989 to 1.355, and Mean Absolute Error (MAE) from 4.250 to 0.932. The random forest model has a better migration effect in similar scenarios. Using a model with a surface type that is more similar to the sensor to be calibrated, the average R-squared obtained by calibrating 34 sensors is 0.946, and the average MAE is 1.584. At the same time, the distance between the sensor to be calibrated and the best-performing migration model was also considered, with the farthest straight-line distance being 94km and the nearest being 7km.This study introduces a calibration method for low-cost meteorological integrated sensors for long-term complex field environment monitoring. Moreover, we compared the migration effect of the random forest model in different typical scenes in the field. Similar surface types are more beneficial to model migration. Even in locations far apart, our model still has stable performance. The results show that this method can significantly improve data quality and increase user confidence in low-cost environmental sensor applications.

To support the current population growth, modern agriculture must increase food production while reducing the use of water and other resources required for crop cultivation. Precision agriculture (PA) aims to achieve these via a variety of methods that include site-specific plant selection, variable rate irrigation and fertilisation, as well as site-specific pesticide and herbicide application. To determine the plant performance and health that drive such precision PA practices, PA solutions currently collect and analyse data from cameras and multispectral sensors. Technological advancements in the Internet of Things (IoT) and in the development of Unmanned Aerial Vehicles (UAV) in recent years have provided potential solutions for automating image acquisition and analysis that can advance such PA practices. This paper proposes 1) devising plant models from RGB and multi-spectral data, 2) using such models to guide the above PA practices. More specifically, the paper explores monitoring plants at different health and life cycle stages from fully green to completely dry and capturing related RGB and multi-spectral data in a controlled environment. These data are then analysed to create a model for each plant variety, which we refer to as the plant profile, that captures the combined colour and light reflectance of the plant over its life cycle and related health stages. The paper proposes using such plant variety profiles to determine the performance and health of the plants across entire crops. Finally, the paper discusses how UAVs and IoT can be used to automatically capture and analyse the images and multi-spectral data for advancing PA.

Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress (e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes (crop, tree, soil, water and road) with the support vector machines (SVMs) algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands (red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information (MI), and full bands of on board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.

Ozone forecast models using nonlinear regression (NLR) have been successfully applied to daily ozone forecast for seven metro areas in Kentucky, including Ashland, Bowling Green, Covington, Lexington, Louisville, Owensboro, and Paducah. In this study, the updated 2005 NLR ozone forecast models for these metro areas were evaluated on both the calibration data sets and independent data sets. These NLR ozone forecast models explained at least 72% of the variance of the daily peak ozone. Using the models to predict the ozone concentrations during the 2005 ozone season, the metro area mean absolute errors (MAEs) of the model hindcasts ranged from 5.90 ppb to 7.20 ppb. For the model raw forecasts, the metro area MAEs ranged from 7.90 ppb to 9.80 ppb. Based on previously developed NLR ozone forecast models for those areas, Takagi-Sugeno fuzzy system models were developed for the seven metro areas. The fuzzy "c-means" clustering technique coupled with an optimal output predefuzzification approach (least square method) was used to train the Takagi-Sugeno fuzzy system. Two types of fuzzy models, basic fuzzy and NLR-fuzzy system models, were developed. The basic fuzzy and NLR-fuzzy models exhibited essentially equivalent performance to the existing NLR models on 2004 ozone season hindcasts and forecasts. Both types of fuzzy models had, on average, slightly lower metro area averaged MAEs than the NLR models. Among the seven Kentucky metro areas Ashland, Covington, and Louisville are currently designated nonattainment areas for both ground level O 3 and PM 2.5 . In this study, summer PM 2.5 forecast models were developed for providing daily average PM 2.5 forecasts for the seven metro areas. The performance of the PM 2.5 forecast models was generally not as good as that of the ozone forecast models. For the summer 2004 model hindcasts, the metro-area average MAE was 5.33ìg/m 3 . Exploratory research was conducted to find the relationship between the winter PM 2.5 concentrations and the meteorological parameters and other derived prediction parameters. Winter PM 2.5 forecast models were developed for seven selected metro areas in Kentucky. For the model fits, the MAE for the seven forecast models ranged from 3.23 ìg/m 3 to 4.61 ìg/m 3 (~26-28% NMAE). The fuzzy technique was also applied on PM 2.5 forecast models to seek more accurate PM 2.5 prediction. The NLR-fuzzy PM 2.5 had slightly better performance than the NLR models.

Accurate, timely, and non-destructive early crop yield prediction at the field scale is essential in addressing changing crop production challenges and mitigating impacts of climate variability. Unmanned aerial vehicles (UAVs) are increasingly popular in recent years for agricultural remote sensing applications such as crop yield forecasting and precision agriculture (PA). The objective of this study was to evaluate the performance of a low-cost UAV-based remote sensing technology for Bambara groundnut yield prediction. A multirotor UAV equipped with a near-infrared sensitive consumer-grade digital camera was used to collect image data during the 2018 growing season (April to August). Flight missions were carried out six times during critical phenological stages of the life-cycle of the monitored crop. Yield was recorded at harvest. Four vegetation indices (VIs) namely normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), green normalized difference vegetation index (GNDVI), and simple ratio (SR) generated from the Red-Green-Near Infrared bands were calculated using the georeferenced orthomosaic UAV images. Pearson’s product-moment correlation coefficient (r) and Bland–Altman testing showed a significant agreement between remotely and proximally sensed VIs. Significant and positive correlations were found between the four VIs and yield, with the strongest relationship observed between SR and yield at podfilling stage (r = 0.81, P < 0.01). Multi-temporal accumulative VIs improved yield prediction significantly with the best index being ∑SR and the best interval being from podfilling to maturity (r = 0.88, P < 0.01). The accumulated ∑SR from podfilling to maturity resulted in higher prediction accuracy with a coefficient of determination (R 2) of 0.71, root mean square error (RMSE) of 0.20 and mean absolute percentage error (MAPE) of 14.2% than SR spectral index at a single stage (R 2 = 0.68, RMSE = 0.24, MAPE = 15.1%). Finally, a yield map was generated using the model developed, to better understand the within-field spatial variations of yield for future site-specific or variable-rate application operations.

Nowadays, it is necessary to use monitoring and forecasting technologies for effective water resources management at water management facilities. The objective of this study is to develop and verify an integrated approach to water resources forecasting with the task of identifying features for forecasting, designing a data preprocessing submodule and a forecasting module. The workflow diagram of the water forecasting system includes sequential stages of data collection, preprocessing, filtering, feature extraction, and training. Sentinel-2 and MODIS satellite sources were used for data preprocessing. Predictors for the formation of time series by normalized difference water index (NDWI) and water surface temperature (LST) were selected in the feature engineering stage. The XGBoost Regressor algorithm was chosen due to its ability to model nonlinear relationships and feature interactions. Excluding winter months improved the model performance for all metrics, which demonstrates the importance of seasonal filtering when working with optical satellite data. The machine learning algorithm takes into account the analysis of satellite data (NDWI and LST indices) through the Google Earth Engine (GEE) platform. Both seasonal and long-term dynamics of water volumes in the Tasotkel reservoir are monitored for the period from 2020 to 2024. In practice, image initial filtering submodules were developed using linear regression and the XGBoost model. Model trained without winter data shows high performance using Metrics Mean Absolute Error (MAE) of 52.793, Root Mean Squared Error (RMSE) of 60.276, coefficient of determination (R2) of 0.673 and Mean Squared Error (MSE) of 3633.252 metrics. However, a decrease in clarity was observed due to snow and ice on reflective properties in winter. For the purpose of rational water resources management, the combination of satellite images and machine learning algorithms in this study shows the prospects for development.

Climate change poses a significant challenge to food security. Several traditional approaches are available to prevent food insecurity in climate change scenarios, such as modifications in crop practices, altering field management systems, and climate-smart farming measures. Recently, precision agriculture practices are becoming a more popular way of farming that might help to overcome climate risk and reduce food insecurity. Precision agriculture is a tool for monitoring the food supply chain and managing both the amount and quality of agricultural products. Thus, this work seeks to synthesize and discuss the research related to the importance of a precision agriculture system in preventing food insecurity in the context of a developing countries such as Pakistan. The study is based on the narrative literature review.

Smart agriculture relies on accurate yield maps as a crucial tool for decision-making. Many yield maps, however, suffer from spatial errors that can compromise the quality of their data, while several approaches have been proposed to address some of these errors, detecting voids or holes in the maps remains challenging. Additionally, the quality of yield datasets is typically evaluated based on root mean squared errors after interpolation. This evaluation method relies on weighbridge data, which can occasionally be inaccurate, impacting the quality of decisions made using the datasets. This paper introduces a novel algorithm designed to identify voids in yield maps. Furthermore, it maps three types of spatial errors (GPS errors, yield surges, and voids) to two standard data quality dimensions (accuracy and completeness). Doing so provides a quality score that can be utilized to assess the quality of yield datasets, eliminating the need for weighbridge data. The paper carries out three types of evaluations: (1) evaluating the algorithm’s efficacy by applying it to a dataset containing fields with and without voids; (2) assessing the benefits of integrating void detection and other spatial error identification techniques into the yield data processing chain; and (3) examining the correlation between root mean squared error and the proposed quality score before and after filtering out spatial errors. The results of the evaluations demonstrate that the proposed algorithm achieves a 100% sensitivity, 91% specificity, and 82% accuracy in identifying yield maps with voids. Additionally, there is a decrease in the root mean squared error when various spatial errors, including voids after applying the proposed data pre-processing chain. The inverse correlation observed between the root mean squared error and the proposed quality score (−0.577 and −0.793, before and after filtering spatial errors, respectively) indicates that the quality score can effectively assess the quality of yield datasets. This assessment enables seamless integration into real-time big data quality assessment solutions based on various data quality dimensions.