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.

A method and system for managing bearing plants so that their in-ground portions (e.g., root-zones) are clustered together to improve water-use effectiveness; and training their above-ground structures such that their above-ground structures have the wider spacing they require. It is applicable to plants (e.g., vine-like plants including wine grapes) whose trunks can be oriented at an angle other than vertical or which can be otherwise shaped so that their distal ends are not centered above their in-ground portions but rather are horizontally offset from the in-ground portions.

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.

The present disclosure provides for, and includes, environments for the production, transport and storage of agricultural products including, but not limited to, fruits, vegetables, grains, tubers, decorative plants, flowers and mushrooms. The present disclosure also relates to methods of preparing environments for the preservation and production of agricultural products Also provided are organic agricultural products having reduced levels of microorganisms and residual organic compounds.

An Internet-of-Things management and control system for an intelligent orchard includes a server, agricultural machinery equipment, an image acquisition apparatus disposed on the site, and various sensors. The agricultural machinery equipment, the image acquisition apparatus and the various sensors are in communication connection with the server. The server includes an orchard management subsystem and an information monitoring subsystem. The orchard management subsystem includes a fruit tree planting planning module, a task management module and various information management modules, and the information monitoring subsystem includes a meteorological environment monitoring module, a soil moisture monitoring module and a disease and pest monitoring module. According to the Internet-of-Things management and control system, all-round management for an orchard from planning to picking can be achieved.

Systems, methods, and/or processes for agricultural parameter determination are provided. One or more can include or use processing circuitry configured to: manage one or more land parcels for agricultural parameter optimization (such as soil organic carbon optimization); and determine one or more agricultural parameters of at least a portion of one land parcel (such as an alleyway between commodity crops) during a first time period without sampling the portion of the one land parcel. Systems, methods, and/or processes for increasing soil organic carbon are also provided.

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.

The present disclosure relates to a method for building a farmland ecosystem with multiple mutual-benefit species in multiple habitats, and belongs to the technical field of agroecological environment. The present disclosure improves the resource utilization efficiency of the farmland ecosystem to the maximum extent, greatly reduces application of chemical fertilizer and pesticides, and reduces environmental damage and pollution. By means of different ecological niches of living things, a beneficial ecological service function is brought into play such that species in the farmland ecosystem can live in a suitable farmland microenvironment, competition for the same living space in the same place is avoided, and harmonious coexistence among the living things is realized; and food with high quality and a higher grain output value are provided.

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.

A method for building a farmland ecosystem with multiple mutual-benefit species in multiple habitats is provided. The method includes: marking out an ecological field plot for planting crops in a farmland, arranging one to three earthworm breeding strip stack(s) at equal intervals, and marking out different planting areas; digging an ecological field ditch surrounding a periphery of the ecological field plot, and planting aquatic plants and breeding aquatic animals in the ecological field ditch; surrounding a periphery of the ecological field ditch with an ecological wide ridge, planting forage plants on a ridge surface of the ecological wide ridge, and planting arbors on an outer side of the ecological wide ridge; and arranging an ecological pond on a drainage side of a hole, wherein aquatic plants are planted in the ecological pond, and crustaceans are bred in the ecological pond.