Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

A Robotic System for In-Situ Measurement of Soil Total Carbon and Nitrogen

Surges in the cost of fertilizer in recent times coupled with the environmental effects of their over-application have driven the need for farmers to optimize the amount of fertilizer they apply on the farm. One of the key steps in determining the right amount of fertilizer to apply in a given field is measuring the amount of nutrients present in the soil. To ascertain nutrient deficiencies, most farmers perform wet chemistry analysis of soil samples which requires a lot of time and is expensive. In this research project, a robotic system was designed and developed that could autonomously move to predetermined GPS waypoints and estimate total carbon (TC) and total nitrogen (TN) content in the soil in-situ using visible and near-infrared reflectance spectroscopy - a faster and cheaper method to determine soil nutrients in real-time. For the locomotion of the robotic system, a Husky robotic platform by Clearpath Robotics was used. A Gen2 robotic arm by Kinova Robotics was used for the precise positioning of the probe in taking soil spectral measurement. The probe was custom designed and built to be used in conjunction with the robotic arm as an end-effector. Two lightweight and inexpensive spectrometers by OceanInsight, namely, Flame VisNIR and Flame NIR+, were used to capture the spectral signatures of soil. The prediction was done with a spectroscopic calibration model and External Parameter Orthogonalization (EPO) was applied to remove the moisture effect from the soil spectra. The robotic system was tested at University of Nebraska-Lincoln (UNL) NU-Spidercam phenotyping facility. Two sets of spectra were obtained from the field campaign: in-situ and dry-ground spectra. The dry-ground spectra were used as library scans and Partial Least Square Regression (PLSR) was used for the modeling. The in-situ spectra were randomly divided into EPO calibration and validation sets. Satisfactory results were obtained from the initial prediction on dry-ground validation set, with R2 (coefficient of determination) of 0.77 and RMSE (Root Mean Squared Error) of 0.15% for TC and R2 of 0.64 and RMSE of 171 ppm for TN. There was a reduction in R2 and an increase in RMSE values for both TC and TN when prediction was done directly on the in-situ validation set. For TC, the R2 dropped and RMSE increased to 0.25 and 0.29% respectively, and for TN, the R2 dropped and RMSE increased to 0.19 and 259 ppm respectively. This was primarily due to the presence of moisture in the field samples. The R2 increased to 0.62 and RMSE decreased to 0.2% for TC, and the R2 increased to 0.51 and RMSE decreased to 200 ppm for TN, when EPO was applied on both the in-situ validation and dry-ground sets. These findings highlight the importance of accounting for moisture effects in the prediction of soil properties using the robotic system and demonstrate the potential of the system in enabling soil monitoring and analysis in-situ. Advisor: Yufeng G

Site-specific seeding using multi-sensor and data fusion techniques : a review

Site-specific seeding (SSS) is a precision agricultural (PA) practice aiming at optimizing seeding rate and depth, depending on the within field variability in soil fertility and yield potential. Unlike other site-specific applications, SSS was not adopted sufficiently by farmers due to some technological and practical challenges that need to be overcome. Success of site-specific application strongly depends on the accuracy of measurement of key parameters in the system, modeling and delineation of management zone maps, accurate recommendations and finally the right choice of variable rate (VR) technologies and their integrations. The current study reviews available principles and technologies for both map-based and senor-based SSS. It covers the background of crop and soil quality indicators (SQI), various soil and crop sensor technologies and recommendation approaches of map-based and sensor-based SSS applications. It also discusses the potential of socio-economic benefits of SSS against uniform seeding. The current review proposes prospective future technology synthesis for implementation of SSS in practice. A multi-sensor data fusion system, integrating proper sensor combinations, is suggested as an essential approach for putting SSS into practice

Application of image processing methodologies for fruit detection and analysis

En aquesta memòria es presenten diversos treballs d'investigació centrats en l’automatització d’operacions agrícoles mitjançant l’aplicació de diverses tècniques de processament d’imatge. En primer lloc es presenta un mètode desenvolupat per detectar i comptar raïms mitjançant la localització de pics d'intensitat en superfícies esfèriques. En segon lloc es desenvolupa un sistema de recol•lecció automàtica de fruita mitjançant la combinació d'una càmera estereoscòpica de baix cost i un braç robòtic. En tercer lloc es proposa una aplicació en què es desenvolupa un mètode basat en l'ús de la informació de color per a la verificació d'una varietat de nectarines de forma automàtica i individual en una línia d’embalatge de fruita. Finalment s’han estudiat les correlacions entre els paràmetres de qualitat de la fruita i el espectre visible de la seva pell amb l’objectiu de controlar la seva qualitat de forma no destructiva durant el seu emmagatzematge.En esta memoria se presentan diversos trabajos de investigación centrados en la automatización de operaciones agrícolas mediante la aplicación de distintas técnicas de procesado de imágenes. En primer lugar se presenta un método desarrollado para detectar y contar uvas rojas mediante la identificación de picos de intensidad en las superficies esféricas. En segundo lugar se desarrolla un sistema de recolección automática de fruta mediante la combinación de una cámara estereoscópica de bajo coste y un brazo robótico. En tercer lugar se propone una aplicación en la que se desarrolla un método de procesamiento de imágenes basado en el uso de la información de color para la verificación de una variedad de nectarinas de forma automática e individual en una línea de envasado de fruta. Finalmente se han estudiado las correlaciones entre los parámetros de calidad de la fruta y el espectro visible de su piel con el fin de controlar su calidad de forma no destructiva durante el almacenamiento.This memory introduces several research works developed to automate agricultural tasks by applying image processing techniques. In the first place a new image processing method is proposed for detecting and counting red grapes by identifying specular reflection peaks from spherical surfaces. The proposal of the second application is to develop an automatic fruit harvesting system by combining a low cost stereovision camera and a robotic arm. The third application proposed is to develop a novel image processing method based on the use of color information to verify an in-line automatic and individual nectarine variety verification in a fruitpacking line. Finally, a study focused on assessing correlations between post-storage fruit quality indices and the visible spectra of the skin of the fruit is proposed in order to control fruit quality in a non-destructive way during the storage

IoT-based systems for soil nutrients assessment in horticulture

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.info:eu-repo/semantics/publishedVersio