A method for the production of fruit trees including the steps of: grafting of an offshoot or propagation material of the variety to be propagated on a grafting point of a rootstock, to form a tree, planting of the rootstock in a nursery, pot or greenhouse ground and subsequent transplanting of the developed tree in a production ground; wherein the planting takes place arranging the rootstock tilted to one side to form with the ground an angle of pre-set amplitude, smaller than 90° and preferably of 45° and with the offshoot in a substantially vertical position; in the transplanting step the rootstock is arranged in the production ground in a substantially vertical position, so that the trunk of the tree, which is angled in respect to the rootstock, forms a main side branch from which vine-like branches or side branches orientated upwards sprout.

The disclosure provides methods for assessing the exposure of red grapes by measuring the amounts of 4′-OH flavonol, 3′4′-OH flavonol, and 3′4′5′-OH flavonol extracted from red grape skins. The disclosure also describes use of Partial Least Squares (PLS) regression for determining the relative percentages of 4′-OH flavonol, 3′4′-OH flavonol, and 3′4′5′-OH flavonol.

A black pepper trellis system. A method of growing black pepper plants is described including constructing a continuous trellis system including vertical and horizontal vine support lines, where the vertical lines are up to 5 meters high and are up to 50 centimeters apart, covering up to 20 acres of soil or more, and growing pepper vines up and across the vertical and horizontal lines, resulting in a dense hedgerow of pepper plants and enhanced black pepper yield per acre. The trellis system and black pepper per acre produced by the above system processes is also described.

An Integrated dual-use machine is for harvesting and treating fruit on trees, in order to harvest the fruit when in season and treat the fruit on the tree with phytosanitary products. The machine includes a moveable structure, intended to shelter the fruit trees. The moveable structure includes a collection unit for the fruit that has been brought down, and is supported on one or two traction vehicles. The vehicles include at least one device for projecting and orienting air currents, at least one infrared camera, at least one collection belt, at least one load container, at least one conveyor belt that connects the collection belt to a load trailer, and a traction system that actuates tractor treads.

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 computer system obtains, using a camera, a first plurality of images of a canopy an agricultural plot. For each respective fruit of a plurality of fruit growing in the agricultural plot, the computer system identifies a first respective image in the first plurality of images that comprises the respective fruit. The first respective image has a corresponding first camera location. The computer system also identifies a second respective image in the first plurality of images that comprises the respective fruit. The second respective image has a corresponding second camera location. The computer system uses at least i) the first and second respective images and ii) a distance between the first and second camera locations to determine a corresponding size of the respective fruit.

A computer system obtains, in electronic format, a training dataset. The training dataset comprises a plurality of training images from a plurality of agricultural plots. Each training image is from a respective agricultural plot in the plurality of agricultural plots and comprises at least one identified fruit. The computer system determines, for each respective fruit in each respective training image in the plurality of training images, a corresponding contour. The computer system trains an untrained or partially trained computational model using at least the corresponding contour for each respective fruit in each respective training image in the plurality of training images, thereby obtaining a first trained computational model that is configured to identify fruit in agricultural plot images.

The present invention relates to a decision support method for the management of an orchard of fruit trees, the method comprising the following steps: Receiving (10) a plurality of digital images acquired in color, Then for each current image of the plurality of images: Converting (20) the current digital image acquired, into a converted HSL image coded in Hue Saturation and Lightness components, Filtering (30) the Hue Saturation and Lightness components, Combining (40) the filtered Hue Saturation and Lightness planes (5, 6, 7) to obtain a Combined plane (12), Calculating (50) a Work plane (13) from the Combined plane (12), Counting (60) the objects of interest in the Work plane (13), Determining (70) a blossoming level for each current image as a function of the number of objects of interest counted for said current image.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying an agricultural object being associated with a subset of agricultural objects, identifying an action corresponding to a subset of agricultural objects responsive, selecting an emitter with which to perform an action associated with the agricultural object, configuring an agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept an agricultural object.

A photo-selective light spectrum-modifying net for use in citrus fruit production, the net comprising a woven array of parallel and mutually spaced first threads and an array of parallel and mutually spaced second threads, the second threads being inclined to the first threads to define an array of openings between the first and second threads, wherein the first threads are uncolored and composed of a first polymer which is selected from a polymer incorporating a white pigment or dye, a transparent polymer or a translucent polymer, and the second threads are colored red and composed of a second polymer which incorporates a red pigment or dye, the second threads being adapted to transmit, scatter and reflect electromagnetic radiation in the wavelength range of from 640 to 680 nm. Also disclosed is a method of producing citrus fruit using the net.