The present invention relates to a method for preparing for harvesting of forest using an un-manned vehicle (100) configured to move under the canopy in a forest region, the method comprising: for each of at least one object (110) within the forest region: obtaining, using at least one sensor (120) of the un-manned vehicle (100), information associated with the object (110); assigning an object identity (ID) to the object (110) based on the obtained sensor information, using processing circuitry (210) comprised in or accessible to the un-manned vehicle (100); and 10associating a marker (130) with the object (110) and the obtained sensor information or the object identity (ID) assigned to the object (110). The invention also relates to an un-manned vehicle, a harvesting system and a non-transitory computer-readable storage medium.

Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

A method and system including: defining a geographic area; receiving a plurality of images; determining a plurality of image points; partitioning the geographic area into a plurality of image regions based on the plurality of image points; and stitching the plurality of images into a combined image based on the plurality of image regions.

There is provided an industrial machinery system including an industrial machine, an area information acquiring device that acquires information on an operation area where the industrial machine is to be caused to perform an operation, and a control apparatus that determines whether to perform the operation by the industrial machine in the operation area, wherein the control apparatus includes a storage section that stores information on a registered area, the information being acquired in advance, and a determining section that determines whether to perform the operation by the industrial machine in accordance with whether an area corresponding to the operation area is identifiable from the registered area stored in the storage section.

A system and method for determining specific prescriptions for targeted areas/plants within given irrigation areas. According to preferred embodiments, the system may use imaging data in combination with other sensors and analysis modules to identify selected pests and diseases affecting given crops. Preferably, the system may use machine learning/AI modules to analyze the data and to provide targeted prescriptions for targeted groups of identified crops based on identified conditions of infestation and/or disease within the identified crops.

One variation of a method for identifying stressors in crops based on fluorescence of sensor plants includes: accessing a set of spectral images of a sensor plant sown in a crop, the sensor plant of a sensor plant type including a set of promoters and a set of reporters configured to signal a set of stressors present at the sensor plant, the set of promoters and set of reporters forming a set of promoter-reporter pairs; accessing a reporter model linking characteristics extracted from the set of spectral images of the sensor plant to the set of stressors based on signals generated by the set of promoter-reporter pairs in the sensor plant type; and identifying a first stressor, in the set of stressors, present at the sensor plant based on the reporter model and characteristics extracted from the set of spectral images.

Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

An agricultural support system includes an unmanned aerial vehicle including a sensor, and an agricultural machine to travel in an agricultural field. When an abnormality occurs in the unmanned aerial vehicle while the agricultural machine performs work in the agricultural field in cooperation with the unmanned aerial vehicle, the unmanned aerial vehicle or the agricultural machine performs an operation different from an operation during the work.

We describe a propeller-thrust-governing system (PTGS) for a propeller that is part of an aircraft. The PTGS includes one or more control surfaces that are located within an airflow of the propeller. The control surfaces are adjustable to reduce thrust produced by the propeller and are also adjustable to redirect the thrust.

This invention relates to insect-like robots empowered by generative artificial intelligence (Gen-AI), specifically designed to address critical challenges in agriculture and environmental monitoring. The robotic bee, mantis, and dragonfly can autonomously perform essential tasks such as crop pollination, pest control, and detailed farmland inspection. These robots feature lifelike designs with components such as heads with integrated high-resolution cameras, antennae for communication, specialized mouthparts, thoraxes housing CPUs and actuators, and wings with thin-film photovoltaic solar materials. The AI models function as the brains, processing data captured by various sensors to provide real-time guidance and control commands. Leveraging advanced technologies and sustainable power sources, these robotic insect-like robots enhance productivity, sustainability, and efficiency in agricultural practices, contributing to a more secure and eco-friendly future.