Research on precise phenotype identification and growth prediction of lettuce based on deep learning.

In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of phenotypes, which serve as external representations of a crop's growth, development, and genetic characteristics, are crucial for the realization of precision agriculture. Applications surrounding phenotypic indices also provide significant technical support for optimizing crop cultivation management and advancing smart agriculture, contributing to the efficient and high-quality development of precision agriculture.

Design of IoT Gateway for Crop Growth Environmental Monitoring Based on Edge-Computing Technology.

With the extensive use of the Internet of Things (IoT) in agriculture, the number of terminals are also grow rapidly. This will increase the network traffic and computing pressure of the centralized server. The centralized data processing mode used in traditional agriculture cannot meet the needs of the Internet of everything era.

Bionic Nonsmooth Drag Reduction Mathematical Model Construction and Subsoiling Verification.

In this study, a bionic nonsmooth drag-reducing surface design method was proposed; a mathematical model was developed to obtain the relationship between the altitude of the nonsmooth drag-reducing surface bulges and the spacing of two bulges, as well as the speed of movement, based on which two subsoiler shovel tips were designed and verified on field experiments. The mechanism of nonsmooth surface drag reduction in soil was analyzed, inspired by the efficient digging patterns of antlions. The nonsmooth surface morphology of the antlion was acquired by scanning electron microscopy, and a movement model of the nonsmooth surface in soil was developed, deriving that the altitude of the nonsmooth drag-reducing surface bulge is proportional to the square of the distance between two bulges and inversely proportional to the square of the movement speed.

A trace CH detection system based on DAS calibrated WMS technique.

We report the development of a compact near-infrared (NIR) laser-based trace methane (CH) detection system. This detection system relied on a 2334 nm distributed feedback (DFB) fiber laser as the light source. A parallel dense light-spot pattern multipass gas cell (MGC) with 41.

Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.

In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data.

Sweet sorghum (Sorghum bicolor L.) SbSTOP1 activates the transcription of a β-1,3-glucanase gene to reduce callose deposition under Al toxicity: A novel pathway for Al tolerance in plants.

Aluminum (Al) toxicity is a primary limiting factor for crop production in acid soils. Callose deposition, an early indicator and likely a contributor to Al toxicity, is induced rapidly in plant roots under Al stress. SbGlu1, encoding a β-1,3-glucanase for callose degradation, showed important roles in sorghum Al resistance, yet its regulatory mechanisms remain unclear.

Mode of Managing Nutrient Solution Based on N Use Efficiency for Lettuce ().

In aeroponic cultivation the nutrient solution is an essential component to achieve good production results. And nitrogen is the main constituent element of the nutrient solution and essential element in plant nutrition. Therefore, the management and monitoring nutrient solution and existing nitrogen is fundamental.

Characterization of microbial community succession during vermicomposting of medicinal herbal residues.

Large amounts of medicinal herbal residues (MHR) are produced in the world annually due to the increasing demand for herbal products. In this study, vermicomposting was used to stabilize MHR. Four inoculating density of earthworms was studied, specifically, 0 (W1), 60 (W2), 120 (W3) and 180 (W4) earthworms per kilogram of substrate.

[Prediction Model on Net Photosynthetic Rate of Soybean Plant Groups Based on Kernel Function and Visible Light Spectrum].

The paper uses MSR-16 portable multispectral radiometer made in the USA and computes the numbers of the test units by pulling the formula on the radiometer effective observation area, which solves the problem on the uncertain numbers of computing the times on region visible light band spectral radiation ratio M_D. The paper uses CI-310 portable photosynthesis measurement system made by American CID Company and measures the net photosynthetic rate of a group of soybean plant. M_D and C_D are normalized by the normalization method [0,1].

[Prediction of Greenhouse Cucumber Disease Based on Chlorophyll Fluorescence Spectrum Index].

The occurrence of greenhouse vegetable diseases and its epidemic seriously affect the production and management of facility agriculture, which greatly reduce the economic benefits of facility agriculture. In order to achieve nondestructive and accurate prediction of greenhouse vegetable diseases, this paper taking cucumber downy mildew disease as the research object, constructed spectrum characteristic index by using chlorophyll fluorescence induced by laser and established the prediction model of greenhouse vegetable diseases. In this paper, the experiment used comparative analysis method.

[Rice blast prediction model based on analysis of chlorophyll fluorescence spectrum].

In order to detect rice blast more rapidly, accurately and nondestructively, the identification and early warning models of rice blast were established in the present research. First of all, rice blast was divided into three grades according to the relative area of disease spots in rice leaf and laser induced chlorophyll fluorescence spectra of rice leaves at different disease levels were measured in the paddy fields. Meanwhile, 502-830 nm bands of laser-induced chlorophyll fluorescence spectra were selected for the study of rice blast.

[Fluorescence spectrum monitor for early warning of greenhouse cucumber aphis pests].

The infection and degree of cucumber aphis pests was studied by analyzing chlorophyllfluorescence spectrum in greenhouse. Based on the configuration of the spectrum, characteristic points were established, in which the intensity of waveband F632 was the first characteristic point between healthy and aphis pests leaves. The second characteristic point was K which was the change rate of spectral curve from waveband F512 to F632.

[Chlorophyll fluorescence spectrum analysis of greenhouse cucumber disease and insect damage].

The present paper is based on chlorophyll fluorescence spectrum analysis. The wavelength 685 nm was determined as the primary characteristic point for the analysis of healthy or disease and insect damaged leaf by spectrum configuration. Dimensionality reduction of the spectrum was achieved by combining simple intercorrelation bands selection and principal component analysis (PCA).

[Cucumber downy mildew prediction model based on analysis of chlorophyll fluorescence spectrum].

In order to achieve quick and nondestructive prediction of cucumber disease, a prediction model of greenhouse cucumber downy mildew has been established and it is based on analysis technology of laser-induced chlorophyll fluorescence spectrum. By assaying the spectrum curve of healthy leaves, leaves inoculated with bacteria for three days and six days and after feature information extraction of those three groups of spectrum data using first-order derivative spectrum preprocessing with principal components and data reduction, principal components score scatter diagram has been built, and according to accumulation contribution rate, ten principal components have been selected to replace derivative spectrum curve, and then classification and prediction has been done by support vector machine. According to the training of 105 samples from the three groups, classification and prediction of 44 samples and comparing the classification capacities of four kernel function support vector machines, the consequence is that RBF has high quality in classification and identification and the accuracy rate in classification and prediction of cucumber downy mildew reaches 97.

[Prediction model of net photosynthetic rate of ginseng under forest based on optimized parameters support vector machine].

Using K-fold cross validation method and two support vector machine functions, four kernel functions, grid-search, genetic algorithm and particle swarm optimization, the authors constructed the support vector machine model of the best penalty parameter c and the best correlation coefficient. Using information granulation technology, the authors constructed P particle and epsilon particle about those factors affecting net photosynthetic rate, and reduced these dimensions of the determinant. P particle includes the percent of visible spectrum ingredients.

[Diagnosis of cucumber diseases and insect pests by fluorescence spectroscopy technology based on PCA-sVM].

The diagnosis model of the cucumber diseases and insect pests was established by laser-induced chlorophyll fluorescence (LICF) spectroscopy technology combined with support vector machines (SVM) algorithm in the present research. This model would be used to realize the fast and exact diagnosis of the cucumber diseases and insect pests. The noise of original spectrum was reduced by three methods, including Savitzky-Golay smoothing (SG), Savitzky-Golay smoothing combined with fast Fourier transform (FFT) and Savitzy-Golay smoothing combined with first derivative transform (FDT).