A method is provided for hyperspectral inversion of a phosphorus content of rubber tree leaves. The method includes: acquiring hyperspectral data of to-be-detected rubber tree leaves; extracting key wavelengths of the rubber tree leaves according to the hyperspectral data and a pre-established wavelength extraction model, where the key wavelengths are related to the phosphorus content of the rubber tree leaves, and the pre-established wavelength extraction model is obtained by learning and training hyperspectral sample data and sample phosphorus content data pairs in a pre-established sample database by adopting a competitive adaptive reweighted sampling (CARS) algorithm and a successive projection algorithm (SPA); and inputting the key wavelengths into a pre-established phosphorus content prediction model to calculate the phosphorus content of the to-be-detected rubber tree leaves. Moreover, the CARS algorithm and the SPA are comprehensively applied to extract the key wavelengths closely related to the phosphorus content of the rubber tree leaves.

A precision agriculture support system is provided with a measuring device, a storage device and a plant species determining unit. The measuring device measures a first spectral characteristic of light derived from vegetation in a support target area. The storage device stores a database of spectrum according to species that shows a spectral characteristic of a desired crop. The plant species determining unit determines whether a plant included in the vegetation is the desired crop or not based on the database of spectrum according to species and a measurement result of the first spectral characteristic. The plant species determination unit further carries out distinction of agricultural crops, distinction of agricultural crops and weeds and the like. Furthermore, the precision agriculture support system identifies an area where abnormality is occurring, estimates a nature of the abnormality and carries out an early warning by providing a countermeasure against the abnormality.

A pesticidal protein class of PirA, PirB, and PirAB fusion proteins exhibiting toxic activity against Coleopteran, Lepidopteran, and Hemipteran pest species is disclosed. DNA constructs are provided which contain a recombinant nucleic acid sequence encoding the PirA, PirB, and PirAB fusion proteins. Transgenic plants, plant cells, seed, and plant parts resistant to Coleopteran, Lepidopteran, and Hemipteran infestation are provided which contain recombinant nucleic acid sequences encoding the PirA, PirB, and PirAB fusion proteins. Methods for detecting the presence of the recombinant nucleic acid sequences or the proteins of the present invention in a biological sample, and methods of controlling Coleopteran, Lepidopteran, and Hemipteran species pests using the PirA, PirB, and PirAB fusion proteins are also provided.

A system and method for preparing a sample area of a bale in order to more accurately evaluate the plant material incorporated into the bale. A baler receives, compresses, shapes, and secures material into the bale. A cutter mechanism cuts a portion of the material in the bale into similarly-sized particles. A mixer mechanism mixes the particles into a homogenous aggregate. A compression mechanism compresses the homogenous aggregate into the bale. An NIR testing system receives and analyzes near-infrared radiation reflected by the homogenous aggregate, and generates evaluation data reflecting properties of the material. The cutter may include knives mounted in a compression chamber of the baler. The mixer may be a relief feature on a center rail, the compressor may be a projecting feature on the center rail, and an NIR sensor may be mounted to the center rail so as to press against the surface of the bale.

In order to achieve a more effective application of a crop efficiency product, a computer-implemented method is provided for applying a crop efficiency product to at least one crop in a field. The method comprises the steps of collecting remotely-sensed data of the field before an application of the crop efficiency product in the field, determining, based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field, generating, for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product, deciding, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.

A verification method of an allelopathic inhibition mechanism based on ecological stoichiometric equilibrium interference is disclosed. By taking ecological stoichiometric characteristics and growth characteristics of Phalaris arundinacea under an equilibrium state as a control, a relative coefficient of variation (RCv) is used to characterize an interference intensity of different intensities of allelopathic stress on ecological stoichiometric equilibrium of the P. arundinacea and an inhibitory intensity of different intensities of allelopathic stress on growth of the P. arundinacea. Then, through correlation analysis among parameters including the intensity of the allelopathic stress, the ecological stoichiometric equilibrium interference, and growth inhibition, a method is provided to verify whether a new mechanism of allelopathic inhibition based on the ecological stoichiometric equilibrium interference exists.

Provided are an image processing apparatus, an image processing method, and an image processing program capable of achieving high accuracy in an index representing vegetation. An image processing apparatus (1) includes a normal map generation unit (12) and a reflection characteristic model generation unit (18). The normal map generation unit (12) obtains a normal vector characteristic based on a polarized image acquired. The reflection characteristic model generation unit (18) estimates a reflection characteristic model based on the normal vector characteristic obtained by the normal map generation unit (12).

A method of measuring element concentration in plant leaves comprises steps of: (a) gathering leaves of plants to be tested; (b) conditioning specimens of said leaves; (c) obtaining raw count-per-second XRF datasets of said specimens; (d) obtaining raw NIR datasets of said specimens; (e) obtaining raw analytical datasets; and (f) assessing concentrations of minerals within said specimens on the basis of said count-per-second XRF, NIR and analytical datasets. The aforesaid method further comprises steps of obtaining white reference radiance datasets and normalizing said raw NIR datasets on the basis thereof and providing NIR reflectance datasets.

A crop monitoring system includes one or more sensors adapted to sense VOCs released by a growing crop and generate a detection signal that is representative of the sensed VOCs. A processor is configured to generate a nutrient status indicator from the detection signal. A user interface device is configured to display the nutrient status indicator for use in a crop management system.

Systems and method for automatically determining offset correction values in a differential measurement system, and for correcting measurement offsets between two measurement devices in the differential measurement system. A method for determining real-time offset corrections in a gas analysis system having first and second gas analyzers includes for each of a plurality of gas concentrations within a range of gas concentrations: a) supplying the concentration of gas to the first and second gas analyzers through first and second gas flow lines, respectively; b) measuring a first gas concentration value using the first gas analyzer; and c) measuring a second gas concentration value using the second gas analyzer. The method may also include determining an offset value between each corresponding first and second gas concentration value, and determining a functional relationship between the offset values and gas concentration measurements of the first gas analyzer.