Systems, and methods for controlling a modular system for improved real-time yield monitoring and sensor fusion of crops in an orchard are disclosed. According to some embodiments of the invention, a modular system for improved real-time yield monitoring and sensor fusion may include a collection vehicle, a modular processing unit, a volume measurement module, a three-dimensional point-cloud scanning module, an inertial navigation system, and a post-processing server. As the collection vehicle travels through an orchard, the volume measurement module calculates volume measurements of the windrow, the three-dimensional point-cloud scanning module assembles point-clouds of each plant in the orchard, and the inertial navigation system calculates geodetic positions of the collection vehicle. The modular processing unit may fuse the collected data together and transmit the fused data set to a post-processing server. The post-processing server may process the geodetic position data for errors which may be used for geo-referencing the fused data.

The unique methodology and utility seeking patient protection in this application is using satellite imagery to quantify and derive harvest statistics of sun-drying crops. High resolution imagery can be used to geospatially define known coffee drying basins however it is impractical for continuous observation as it is costly and infrequent. The geospatially defined regions of interests of coffee drying basins can be matched with more frequent, lower resolution, multispectral satellite imagery (such as Sentinel-2). The signals can be tested against the known spectral signatures of washed and unwashed coffee to determine whether or not each pixel contains coffee. The result will yield a classified region of interest which can be used to determine the quantity of drying coffee and the washed to unwashed ratio of a harvest. With regular monitoring across multiple temporal scenes the harvest's seasonality and historical change can be derived.

The method of determining the presence of a spill of a petroleum product by the detection of a petroleum-derived volatile organic compound (VOC) generally has a step of providing an ultraviolet (UV) radiation generator and a receiver assembly aimed at a scene; a step of illuminating a distant target in the scene with a UV radiation beam, the UV radiation beam having an excitation wavelength being tuned to a resonance Raman excitation wavelength of the petroleum derived VOC; a step of receiving a return signal from the distant target; and a step of determining the presence of the petroleum-derived VOC upon detecting Raman scattering in the received return signal, the Raman scattering being indicative of a resonance Raman interaction between the UV radiation beam and molecules of the petroleum-derived VOC.

There is provided an information processing apparatus including a macro measurement analysis calculation section configured to calculate detection data from a macro measurement section adapted to perform sensing at a first spatial resolution for a first measurement range for a measurement target, a micro measurement analysis calculation section configured to calculate detection data from a micro measurement section adapted to perform sensing at a second spatial resolution for a second measurement range, the second spatial resolution being higher than the first spatial resolution, the second measurement range being included in the first measurement range for the measurement target, and an inverse model calculation section configured to acquire a model parameter used for an inverse model calculation using a calculation result from the macro measurement analysis calculation section, on a basis of the detection data from the micro measurement section determined by the micro measurement analysis calculation section.

What is provided is a method and system for more precisely, accurately, and reliably detecting aquatic species, namely infestations of invasive aquatic plants. The system and methods disclosed herein allow for a more effective determination of a treatment plan to reduce any potential negative impact on the aquatic ecology and to minimize any unnecessary human exposure to toxic chemicals.

The present invention relates to systems and methods for monitoring agricultural products. In particular, the present invention relates to monitoring fruit production, plant growth, and plant vitality. According to embodiments of the invention, a plant analysis system is configured determine a spectral signature of a plant based on spectral data, and plant color based on photographic data. The spectral signatures and plant color are associated with assembled point cloud data. Morphological data of the plant can be generated based on the assembled point cloud data. A record of the plant can be created that associates the plant with the spectral signature, plant color, spectral data, assembled point cloud data, and morphological data, and stored in a library.

A vegetation index calculation method is a method for calculating a vegetation index indicating a vegetation state in an earth's surface using an imaging unit and a flight vehicle, and the vegetation index calculation method includes an illumination spectrum acquisition step of acquiring a spectrum of illumination light with which the earth's surface is irradiated and a reflection spectrum acquisition step of acquiring a reflection spectrum of the earth's surface by the illumination light. The vegetation index calculation method also includes a calibration step of calibrating the reflection spectrum using the illumination spectrum and a step of calculating the vegetation index of the earth's surface based on a calibrated spectrum obtained through the calibration step.

Machine learning models for generating predictions of sequestered soil carbon content are provided. The models can be trained on synthetic training data with associated synthetic spectral measurements and ground truth training data with associated ground truth spectral measurements. Systems and methods for training such machine learning models are also provided. Prediction of sequestered soil carbon may be facilitated by using Raman spectral measurements, generating synthetic spectral measurements by physical simulation for training data, using spectral measurements at varying degrees of resolution (e.g. using satellite measurements bolstered by measurements from aerial and/or proximal sensors), and/or modeling uncertainty in the predictions. Synthetic and ground-truth modes of training may differ in the number and type of input provided.

Systems, and methods for controlling a modular system for improved real-time yield monitoring and sensor fusion of crops in an orchard are disclosed. According to some embodiments of the invention, a modular system for improved real-time yield monitoring and sensor fusion may include a collection vehicle, a modular processing unit, a volume measurement module, a three-dimensional point-cloud scanning module, an inertial navigation system, and a post-processing server. As the collection vehicle travels through an orchard, the volume measurement module calculates volume measurements of the windrow, the three-dimensional point-cloud scanning module assembles point-clouds of each plant in the orchard, and the inertial navigation system calculates geodetic positions of the collection vehicle. The modular processing unit may fuse the collected data together and transmit the fused data set to a post-processing server. The post-processing server may process the geodetic position data for errors which may be used for geo-referencing the fused data.

The present invention relates to systems and methods for monitoring agricultural products. In particular, the present invention relates to monitoring fruit production, plant growth, and plant vitality.