The present invention refers to a system of environmental data collection, monitoring and behavioral analysis of animals on pasture, in a georeferenced environment using agrometeorological sensors, with the objective of spreading precision farming. The data generated is collected over a wireless network and stored in a database. The system is capable of analyzing the data of the various sensors to generate information on the cattle's behavior, and can also correlate them with environmental characteristics and data of the management area.

Disclosed herein are compositions including cells of defined sets of microbial species (for example, 3, 16, 18, 19, 21, or 22 microbial species). Also disclosed are methods of using the microbial compositions that include contacting soil, plants, plant parts, or seeds with the composition. The microbial compositions are also used in methods of degrading biological materials, such as chitin-containing biological materials.

The present disclosure is related to compacted calcium-based granules, in particular pellets, spheroidal/lens-shaped pellets, exhibiting specific chemical and physical properties and comprising a source of calcium and/or a source of magnesium, and optionally one or more additives. The present disclosure further relates to a method for the production of said pellets and to the use of said pellets in, for example, the steel industry, agriculture (in particular precision farming) and the glass industry.

A system for implementing a trial in one or more fields is provided. In an embodiment, a agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

A sensor network for measuring and processing agricultural sensor measurements having multi-depth sensors, field monitors, and/or field data collection systems. The multi-depth sensor having a GPS; sets of physical sensors located at different depths; a processing structure sampling measurements from the physical sensors; and storing the measurements. The field monitor for use with a mobile platform having: a housing; a camera; a LiDAR sensor; a processing structure capturing point data and image data; generating above-ground field data; and determining crop data. The field data collection system having a stationary field monitor and one or more mobile field monitors capturing above-ground data. The stationary field monitor and the mobile field monitors having an associated GPS. The multi-depth sensors capture below-ground data and communicating the below-ground data to the stationary field monitor. A GPU processes the above-ground data and the GPS data to generate a point cloud data set.

Methods and compositions for modulating plant meristems. Methods are provided for modulating fea3 and/or Fcp1 genomic locus in a host plant or plant cell to improve agronomic characteristics such as kernel number.

A growth-promoting bacterial agent capable of improving content of soybean oil, preparation method, and use thereof are provided. The growth-promoting bacterial agent is fermented by a DW1 strain, the classification name of the DW1 strain is Bacillus sp., the DW1 strain is deposited in the China Center for Type Culture Collection on Jul. 16, 2021, and the deposit number is CCTCC NO: M 2021889. The growth-promoting bacterial agent of the present disclosure has the ability to dissolve insoluble phosphorus and insoluble potassium and can increase content of readily available phosphorus and readily available potassium in the soil, which has an obvious promoting effect on the growth of soybean and improves soybean yield. The growth-promoting bacterial agent of the present disclosure promotes bacteria and can significantly improve the content of soybean oil. At the same time, it has an affinity of soybean agglutinin and can agglutinate with the soybean agglutinin.

A GNSS/IMU-based tilt measurement system includes a GNSS/IMU receiver. The GNSS/IMU receiver includes a GNSS antenna, a GNSS positioning board, an IMU inertial sensor and a position transfer device. The GNSS antenna is configured to receive a satellite navigation positioning signal. The GNSS positioning board is configured to calculate coordinates of the phase center of the GNSS antenna according to a signal received by the GNSS antenna and use the coordinates of the phase center as reference coordinates for measuring a relative position point. The IMU inertial sensor is configured to measure the acceleration and the angular velocity of the receiver. The relative position transfer medium is configured to connect the reference coordinates and the coordinates of the relative position point to be measured. The position transfer device is configured to implement the measurement function of the relative position point.