Mapping in-field cotton fiber quality and relating it to soil moisture

The overarching goal of this dissertation project was to address several fundamental aspects of applying site-specific crop management for fiber quality in cotton production. A two-year (2005 and 2006) field study was conducted at the IMPACT Center, a portion of the Texas A&M Research farm near College Station, Texas, to explore the spatial variability of cotton fiber quality and quantify its relationship with in-season soil moisture content. Cotton samples and in-situ soil moisture measurements were taken from the sampling locations in both irrigated and dry areas. It was found that generally low variability (CV < 10%) existed for all of the HVI (High Volume Instrument) fiber parameters under investigation. However, an appreciable level of spatial dependence among fiber parameters was discovered. Contour maps for individual fiber parameters in 2006 exhibited a similar spatial pattern to the soil electrical conductivity map. Significant correlations (highest r = 0.85) were found between most fiber parameters (except for micronaire) and in-season soil moisture in the irrigated areas in 2005 and in the dry area in 2006. In both situations, soil moisture late in the season showed higher correlation with fiber parameters than that in the early-season. While this relationship did not hold for micronaire, a non-linear relationship was apparent for micronaire in 2006. This can be attributed to the boll retention pattern of cotton plants at different soil moisture levels. In addition, a prototype wireless- and GPS-based system was fabricated and developed for automated module-level fiber quality mapping. The system is composed of several subsystems distributed among harvest vehicles, and the main components of the system include a GPS receiver, wireless transceivers, and microcontrollers. Software was developed in C language to achieve GPS signal receiving, wireless communication, and other auxiliary functions. The system was capable of delineating the geographic boundary of each harvested basket and tracking it from the harvester basket to the boll buggy and the module builder. When fiber quality data are available at gins or classing offices, they can be associated with those geographic boundaries to realize fiber quality mapping. Field tests indicated that the prototype system performed as designed. The resultant fiber quality maps can be used to readily differentiate some HVI fiber parameters (micronaire, color, and loan value) at the module level, indicating the competence of the system for fiber quality mapping and its potential for site-specific fiber quality management. Future improvements needed to make system suitable for a full-scale farming operation are suggested

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with \u3e43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations

Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization

Moisture is the single most important factor that affects soil reflectance spectra, particularly for field applications. Interest in using soil VNIR spectral libraries, which are commonly based on dry ground soils, to predict soils in the intact field-moist condition (in situ VNIR) is growing. External parameter orthogonalization (EPO) has been proposed as a useful method that links dry ground VNIR models to field moist scans. The goal of this study is to test EPO on a wider set of soil properties and four different modeling techniques, namely, Partial Least Squares Regression (PLS), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM). We selected and scanned 352 archived soil samples fromNebraska, USA, among which 185 samples were used to develop dry groundmodels and the remaining 167 sampleswere rewetted to eight differentmoisture levels for EPO development and testing. Two methods to determine optimum number of EPO components, model-coupled cross validation (Model-Coupled-CV) and Wilk\u27s Λ were also compared. The results showed that EPO minimized the variability of soil spectra induced by moisture. Results suggest a preference for the Wilk\u27s Λ method over Model-Coupled-CV for determining the number of EPO components g, as it produced smoother transformed spectra and more parsimonious models. Among the eight soil properties tested, EPO caused significant improvements for soil Organic Carbon (OC), Inorganic Carbon (IC), and Total Carbon (TC) prediction, marginal improvement for sand and clay, and no improvement for pH, Mehlich-3 Phosphorus, and Cation Exchange Capacity. The failed EPO for the latter three properties is attributable to the poor initial dry-ground models that EPO was built upon. For OC, IC, and TC, EPO coupled effectively with all four modeling methods, with ANN and SVM outperforming the other two slightly. This adds flexibility to the implementation of EPO in predicting field moist soils. As there are increasing demands of spatially-explicit soil data in many disciplines, EPO would be an important essential part for the future in situ VNIR based proximal soil sensing technology

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era

Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors

Interreplicate variability—the spread in output values among units of the same sensor subjected to essentially the same condition—can be a major source of uncertainty in sensor data. To investigate the interreplicate variability among eight electromagnetic soil moisture sensors through a field study, eight units of TDR315, CS616, CS655, HydraProbe2, EC5, 5TE, and Teros12 were installed at a depth of 0.30 m within 3 m of each other, whereas three units of AquaSpy Vector Probe were installed within 3 m of each other. The magnitude of interreplicate variability in volumetric water content (θv) was generally similar between a static period near field capacity and a dynamic period of 85 consecutive days in the growing season. However, a wider range of variability was observed during the dynamic period primarily because interreplicate variability in θv increased sharply whenever infiltrated rainfall reached the sensor depth. Interreplicate variability for most sensors was thus smaller if comparing θv changes over several days that excluded this phenomenon than if comparing θv directly. Among the sensors that also reported temperature and/or apparent electrical conductivity, the sensors exhibiting the largest interreplicate variability in these outputs were characterized by units with consistently above or below average readings. Although manufacturers may continue to improve the technology in and the quality control of soil moisture sensors, users would still benefit from paying greater attention to interreplicate variability and adopting strategies to mitigate the consequences of interreplicate variability

Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging

Automated collection of large scale plant phenotype datasets using high throughput imaging systems has the potential to alleviate current bottlenecks in data-driven plant breeding and crop improvement. In this study, we demonstrate the characterization of temporal dynamics of plant growth and water use, and leaf water content of two maize genotypes under two different water treatments. RGB (Red Green Blue) images are processed to estimate projected plant area, which are correlated with destructively measured plant shoot fresh weight (FW), dry weight (DW) and leaf area. Estimated plant FW and DW, along with pot weights, are used to derive daily plant water consumption and water use efficiency (WUE) of the individual plants. Hyperspectral images of plants are processed to extract plant leaf reflectance and correlate with leaf water content (LWC). Strong correlations are found between projected plant area and all three destructively measured plant parameters (R2 \u3e 0.95) at early growth stages. The correlations become weaker at later growth stages due to the large difference in plant structure between the two maize genotypes. Daily water consumption (or evapotranspiration) is largely determined by water treatment, whereas WUE (or biomass accumulation per unit of water used) is clearly determined by genotype, indicating a strong genetic control of WUE. LWC is successfully predicted with the hyperspectral images for both genotypes (R2 = 0.81 and 0.92). Hyperspectral imaging can be a very powerful tool to phenotype biochemical traits of the whole maize plants, complementing RGB for plant morphological trait analysis

Capturing Spatial Variability in Maize and Soybean using Stationary Sensor Nodes

• Irrigation in agriculture maximizes crop yield and improves food security globally • Irrigation scheduling is strongly based on the ability to accurately estimate the appropriate amount and timing of water application • The timing of the irrigation can best be informed through the crop canopy stress, and the amount of irrigation is informed through soil moisture depletion • Developing upper (non-water stressed) and lower (non-transpiring) baselines for irrigated and non-irrigated maize and soybean • Investigating the relationship between the canopy stress and the soil moisture stress The canopy temperature stress and soil moisture depletion had stronger correlation for non-irrigated treatments in soybean than maiz

High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging

The possibility of predicting plant leaf chemical properties using hyperspectral images was studied. Sixty maize and 60 soybean plants were used, and two experiments were conducted: one with water limitation and the second with nutrient limitation, with the purpose of creating wide ranges of these chemical properties in plant leaf tissues. A hyperspectral imaging system with a spectral range from 550 to 1700 nm was used to acquire plant images in a high throughput fashion (plants placed on an automated conveyor belt). Leaf chemical properties were measured in the laboratory. Partial least squares regression was implemented on spectral data to successfully model and predict water content, micronutrient, and macronutrient concentrations

Data fusion technology for precision forestry applications

Presently precision forestry is playing an important role in realizing sustainable development and improving societal and economical efficiency for forestry applications. Based on analyzing the features of precision forestry's information requirements, the data needed for precision forestry were classified and the characteristics of the different information were summarized. Data fusion for precision forestry was studied in this paper. The architecture for precision forestry information processing, which integrated information fusion and data mining, was put forward. New and emerging technologies such as Remote Sensing (RS), Geographical Information System (GIS), Global Position System (GPS), Data Base Management System (DBMS), Data Fusion, Decision Support Systems (DSS), and Variable Rate technology (VRT) are applied in forestry production as aids in producers' and managers' decision-making process. Precision irrigation, precision fertilizing, precision pesticide application, precision harvesting, and precision deforestation can promote the realization of minimizing resource inputs, minimizing environmental impacts, and maximizing forest outputs

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360◦ view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 \u3e 0.91 for individual leaf area; R2 \u3e 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D