6 results about "Leaf water content" patented technology

The embodiment of the invention provides a leaf moisture content and chlorophyll detection method based on a near infrared spectrum. The method comprises the following steps: acquiring original spectrum data of a sample leaf; preprocessing the original spectral data to obtain processed spectral data; carrying out key wave band screening on the processed spectral data by adopting a competitive self-adaptive reweighted sampling algorithm or a continuous projection algorithm to obtain target spectral data; and inputting the target spectral data into a pre-constructed neural network model, and detecting the water content and chlorophyll of the leaves to obtain a detection result. In the scheme, the CNN-KAN integrates the advantages of the convolutional neural network and the KAN network, so that the nonlinear fitting capability of the model is improved, the interpretability of the model is enhanced, the efficient extraction capability of fine features in the spectral data is shown, the detection cost is reduced, the detection efficiency is high, and field detection can be realized.

This invention discloses a method for inverting equivalent water thickness in plant leaves across species. This method selects equivalent water thickness with more explicit physical meaning as a representative indicator of plant leaf water content. Based on traditional vegetation indices, it combines continuous removal of preprocessing parameters to construct absorption feature parameters, thereby reducing noise in hyperspectral data while deeply mining its inherent spectral characteristics. It innovatively introduces one-heat coding technology to explicitly process species information and employs a Bayesian optimized machine learning model, effectively improving the accuracy, stability, and computational efficiency of cross-species water inversion. This invention can accurately invert the water status of crops across species, providing a new method for rapidly acquiring crop water status based on hyperspectral reflectance characteristics. It can be widely applied in intercropping and relay cropping patterns and large-scale survey and analysis scenarios, greatly improving the accuracy and speed of water stress diagnosis.

This invention discloses a method for improving the curing quality of upper tobacco leaves under arid conditions. This method targets upper leaves in arid climates, characterized by a compact structure, low leaf water content, and water bound to cellular tissue. During the yellowing stage, a dry-bulb temperature of 42-43℃ and a wet-bulb temperature of 38-39℃ are used to keep the stomata on both sides of the leaf open, ensuring the smooth removal of moisture from the tobacco leaf and allowing O2 from the curing barn to continuously enter the leaf through the stomata, aiding in the degradation of macromolecular organic matter and thus guaranteeing the quality of the tobacco leaf. This invention improves the curing quality of upper leaves under arid climates and reduces the occurrence of ash and discoloration during the curing process.

The invention provides a method for rapidly identifying salt tolerance of eggplants. The method comprises the following steps: performing salt stress treatment in a seedling stage of the eggplants; measuring the growth characteristics of the eggplant seedlings after salt stress treatment, wherein the growth characteristics comprise plant height, stem diameter, leaf number, leaf area, root length, root surface area, root volume, root tip number, chlorophyll content, malondialdehyde content, root water content, stem water content and leaf water content; calculating a salt tolerance comprehensive evaluation value according to the growth characteristics; based on the salt tolerance comprehensive evaluation value, salt tolerance classification is conducted on the eggplant seedlings through a correlation analysis method, a principal component analysis method, a membership function analysis method and a clustering analysis method, and classification results are obtained; and based on a classification result, constructing a prediction model through a stepwise linear regression analysis method so as to accurately identify the salt tolerance of the eggplant germplasm resource in the seedling stage. According to the method, through multi-index comprehensive evaluation and mathematical model prediction, the efficiency and accuracy of eggplant salt tolerance identification are improved.

The invention discloses application of a bHLH18 transcription factor in regulation and control of plant stress resistance, and belongs to the technical field of biology. The invention reports the drought resistance positive regulation related protein ZmbHLH18 and the coding gene thereof for the first time. A corn knockout strain and an overexpression strain of the gene are obtained, drought-related physiological indexes are measured, the overexpression of the ZmbHLH18 can improve the drought tolerance of corn, and the overexpression strain shows higher leaf water content, lower water loss rate, higher antioxidant enzyme activity and stronger photosynthesis stability. The physiological indexes directly reflect the survivability of the plants under the drought condition, so that the drought resistance of the plants is enhanced, the positive regulation effect on the drought resistance of the plants is shown, the technical scheme provides a basis for cultivating resistant plants, and a more accurate and quicker way is provided for drought-resistant breeding of corns.

The invention relates to the field of crop irrigation technology and remote sensing, in particular to a potato leaf water content staging monitoring method based on an optimized spectral band. Comprising the following steps: acquiring hyperspectral measured data of leaves; preprocessing the hyperspectral measured data of the leaves respectively; carrying out fusion screening by adopting a continuous projection algorithm SPA and an interval variable iteration space contraction method IVISSA; the optimized spectral band combination is input into a seedling stage leaf water content SVM prediction model, and a leaf water content prediction value in the seedling stage is obtained; inputting into a tuber formation period leaf water content SVM prediction model to obtain a leaf water content prediction value in a tuber formation period; and inputting into a tuber expansion period leaf water content SVM prediction model to obtain a leaf water content prediction value in a tuber expansion period.