Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

A plant growth promoter production method includes: preparing a modified cyanobacterium in which a function of a protein involved in binding between an outer membrane and a cell wall of cyanobacterium is suppressed or lost; and causing the modified cyanobacteria to secrete a secretion involved in promoting growth of a plant.

A modular system includes a hub and a set of modules removably coupled to the hub. The modules are physically coupled to the frame relative to each other so that each module can operate with respect to a different row of a field. An individual module includes a sensor for capturing field measurement data of individual plants along a row as the modular system moves through the geographic region. An individual module further includes a treatment mechanism for applying a treatment to the individual plants of the row based on the field measurement data before the modular system passes by the individual plants. An individual module further includes a computing device that determines the treatment based on the field measurement data and communicates data to the hub. The hub is communicatively coupled to the modules, so that it may exchange data between the modules and with a remote computing system.

The present invention provides a system and method for real-time monitoring of an above-ground height of a boom based on multi-source information fusion. The system includes a boom, an information acquisition unit, and a control unit. The method includes: step 1: establishing a relationship between an above-ground height s of the boom and an output current y of a pull-wire cylinder displacement sensor; step 2: calibrating ultrasonic ranging sensors; step 3: acquiring above-ground heights of the boom; step 4: performing anti-interference processing on the acquired height data; step 5: calculating an above-ground height H.sub.0 of the boom by multi-source data fusion; step 6: calculating a distance H.sub.canno between the boom and a crop canopy; step 7: acquiring an inclination angle θ.sub.b of the boom; and step 8: calculating heights H.sub.end of two ends of the boom relative to ground.

An illustrative smart sprayer for a precision agricultural implement includes a mount for coupling the sprayer to a modular tool arm of the implement, and a spray head, an airflow generator, a solenoid valve, and an inspirator mixer located within a containment shroud. The smart sprayer is one of alternative agricultural tools that can be selectively coupled to and be operated by a control system of the common platform precision agricultural implement, including a machine vision module for identifying and locating commodity plants, non-commodity plants, and positioning the agricultural tools laterally and vertically relative to commodity plant lines.

Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.

The invention provides methods for producing seeds in watermelon. In one embodiment methods are provided comprising grafting of a seed parent onto a stress tolerant rootstock, pollinating the seed parent with pollen from a pollen donor, and cultivating the seed parent until seed is formed. In specific embodiments, triploid seeds produced by a method of the invention are rendered conspicuously distinguishable from tetraploid seeds, and thus readily selected manually or by an automated machine. Methods for increasing seed yield and/or quality are also provided.

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.

The present subject matter relates to systems, devices, and methods for data-driven precision agriculture through close-range remote sensing with a versatile imaging system. This imaging system can be deployed onboard low-flying unmanned aerial vehicles (UAVs) and/or carried by human scouts. Additionally, the present technology stack can include methods for extracting actionable intelligence from the rich datasets acquired by the imaging system, as well as visualization techniques for efficient analysis of the derived data products. In this way, the present systems and methods can help specialty crop growers reduce costs, save resources, and optimize crop yield.

A method for creating precision agriculture data maps utlilizing one or more hand-carried or vehicle transported mobile sensors that feed various types of agriculture-related soil and plant measurements into a mobile processing device that has software that automatically plots the data geospatially and contours the data readings in color-coded or shaded gradients that can be displayed in layers and that can be saved and compared over time.