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.This paper focuses on lettuce and employs common nutritional stress conditions during growth as experimental settings. By collecting RGB images throughout the lettuce's complete growth cycle, we developed a deep learning-based computational model to tackle key issues in the lettuce's growth and precisely identify and assess phenotypic indices. We discovered that some phenotypic indices, including custom ones defined in this study, are representative of the lettuce's growth status. By dynamically monitoring the changes in phenotypic traits during growth, we quantitatively analyzed the accumulation and evolution of phenotypic indices across different growth stages. On this basis, a predictive model for lettuce growth and development was trained.The model incorporates MSE, SSIM, and perceptual loss, significantly enhancing the predictive accuracy of the lettuce growth images and phenotypic indices. The model trained with the reconstructed loss function outperforms the original model, with the SSIM and PSNR improving by 1.33% and 10.32%, respectively. The model also demonstrates high accuracy in predicting lettuce phenotypic indices, with an average error less than 0.55% for geometric indices and less than 1.7% for color and texture indices. Ultimately, it achieves intelligent monitoring and management throughout the lettuce's life cycle, providing technical support for high-quality and efficient lettuce production.

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. This paper designs a gateway based on edge-computing technology for monitoring crop growth environment. It uses virtualized container technology to package long-range wide-area network (LoRaWAN) server, pest identification, and environmental information data fusion functions into images. It forms integrated operation mode of multiple function in agriculture. The gateway applies message-oriented middleware to standardize and customize the data transmission among functional modules, clouds, and edges. Through simulation and field test, the designed gateway can achieve the functions of each module at the same time, the resource utilization, and the transmission quality are stable. The edge-computing gateway has the advantages of low cost, low latency, and low power consumption which has practical significance.

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. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).

Integrated optomechanical analyses and experimental verification for a thermal system of an aerial camera.

The thermal control system based on a combination of passive and active methods for a compact aerial camera used in the unmanned aerial vehicle system is studied. Integrated analysis and an experimental method are developed to ensure both low-power limit and high image quality of the camera. For rapid estimation of thermal behavior, we develop a thermal mathematic model based on a thermal network method that also offers an initial design reference for the active control system; then we develop a more complex integrated analysis method to analyze and optimize the thermal system, which allows us to get performance insights such as internal temperature gradient and airflow of the compact system. We also focus on analyzing the optical surface errors under thermal disturbance. Comparisons of interferometer test records and thermal-elastic simulation results are presented, and this comparison shows that the integrated optomechanical analysis method contributes to the success of optomechanical system design by ensuring thermal disturbance will not deform the optical surfaces beyond allowable limits. Finally, the design method is verified through a thermo-optic experiment.

Improvement of Peptide Separation for Exploring the Missing Proteins Localized on Membranes.

Following an enormous effort by the global scientific community coordinated by HUPO's Human Proteome Project, the number of proteins without high-quality MS or other evidence (colloquially termed missing proteins) has substantially decreased; however, some highly hydrophobic MPs remain on the list. We believe that efficient peptide separation is an approach that can be used to improve the identification of these hydrophobic MPs. We propose that peptides prepared from the membrane fractions of human cell lines and placental tissue can be well separated from hydrophilic peptides in organic solvents at high concentrations due to the precipitation of hydrophilic peptides with lower solubility. Using a combination strategy of peptide separation in 98% acetonitrile prior to traditional 2D reverse-phase liquid chromatography, more hydrophobic peptides were detected in the supernatants of the organic solvent extractions than were found in the pellets. When this strategy was adopted, 30 MPs (≥2 non-nested unique peptides with ≥9 amino acids) with 114 unique peptides were identified at protein false discovery rate (FDR) < 1%, including 7, 12, and 13 MPs obtained from membrane preparations derived from K562, HeLa cells, and human placenta, respectively. Of the 30 MPs identified in this study, 19 were categorized as membrane proteins or extracellular matrix proteins. Furthermore, 20 were verified to possess two non-nested unique peptides through parallel reaction monitoring with the corresponding chemically synthesized peptides. The use of organic solvents at high concentrations was shown to be an efficient way to improve the exploration of hydrophobic MPs. The data obtained in this study are available via ProteomeXchange (PXD010630) and PeptideAtlas (PASS01218).

Assessment of Micro-Basin Tillage as a Soil and Water Conservation Practice in the Black Soil Region of Northeast China.

Micro-basin tillage is a soil and water conservation practice that requires building individual earth blocks along furrows. In this study, plot experiments were conducted to assess the efficiency of micro-basin tillage on sloping croplands between 2012 and 2013 (5°and 7°). The conceptual, optimal, block interval model was used to design micro-basins which are meant to capture the maximum amount of water per unit area. Results indicated that when compared to the up-down slope tillage, micro-basin tillage could increase soil water content and maize yield by about 45% and 17%, and reduce runoff, sediment and nutrients loads by about 63%, 96% and 86%, respectively. Meanwhile, micro-basin tillage could reduce the peak runoff rates and delay the initial runoff-yielding time. In addition, micro-basin tillage with the optimal block interval proved to be the best one among all treatments with different intervals. Compared with treatments of other block intervals, the optimal block interval treatments increased soil moisture by around 10% and reduced runoff rate by around 15%. In general, micro-basin tillage with optimal block interval represents an effective soil and water conservation practice for sloping farmland of the black soil region.

[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. The healthy leaves of the crops were inoculated with the pathogen spores, the spectrum curves of four groups of test samples: healthy, 2 d inoculated, 6 d inoculated and the ones with obvious symptoms were collected; then qualitative analysis was given to the variation regulation of the fluorescence intensity with the leaf samples infected with the pathogen spores. The chlorophyll fluorescence spectrum index k1=F685/F512 and k2=F734/F512 were created by using the peak and valley values of different bands. According to the range of values, set k1=20 and k2=10 as the characteristic value to judge the sample with obvious symptoms or with no obvious symptoms, and the accuracy rate of the judgment was 96% and 94% respectively. Based on spectrum index created and the classification results of sample health status, we selected the spectrum index F685/F512, F685-F734, F715/F612 to determine the health status of the sample and selected spectrum index F685/F512, F734/F512, F685-F734, F715/F612 as the inputs of quantitative analysis model. Regarding classification accuracy of prediction set as the evaluation criteria, we compared three data modeling methods: discriminant analysis, BP neural network and support vector machine. The results showed that the forecasting ability can reach 91.38% when the support vector machine was used as the modeling method for predicting the downy mildew disease. Use the method with chlorophyll fluorescence induced by laser to construct spectrum index to study the prediction of plant diseases, which has a good classification and identification effect.

[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. Savitzky-Golay(SG) smoothing and First Derivative Transform(FDT) were applied for the pretreatment of laser-induced chlorophyll fluorescence spectra. Then the method of Principal Components Analysis (PCA) was used to achieve the dimension reduction on spectral information, three principal components whose variance are greater than 1 and cumulative credibility is 99.924% were extracted by this method. Furthermore, the tentative data were divided into calibration set and validation set, the levels of rice blast were taken as the predictors. Combined with the calibration set which contains the disease and spectral information of 133 leaves, Discriminant Analysis (DA), Multiple Logistic Regression Analysis (MLRA) and Multilayer Perceptron (MLP) were used respectively to establish the identification and early warning models of rice blast. The Prediction examinations of the three models were made based on the validation set which contains the disease and spectral information of 89 leaves. The results show that all the models of PCA-DA, PCA-MLRA and PCA-MLP can carry on the prediction of rice blast, and the average prediction accuracy of PCA-MLP prediction model is 91.7% which is improved compared with PCA- DA and PCA- MLRA.

[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. The early warning could be executed on plants depending on these two points. The models of the infection and degrees of aphis pests were established for different wavebands by the least square support vector machine classification method (LSSVMR) radial basis function(RBF). The accuracy rate of classification and prediction of the models was compared by different peaks and valleys value in wavebands. The results indicated that the prediction accuracy of the model established by waveband F632 was the most perfect (96.34%).

[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). The principal component factor was reduced from 10 to 5 while the spectrum information was kept reaching 99.999%. By comparing and analysing three modeling methods, namely the partial least square regression (PLSR), BP neural network (BP) and least square support vector machine regression (LSSVMR), regarding correlation coefficient of true value and predicted value as evaluation criterion, eventually, LSSVMR was confirmed as the appropriate method for modeling of greenhouse cucumber disease and insect damage chlorophyll fluorescence spectrum analysis.

[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 97.73%.

[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). According to the accumulative reliabilities (AR) seven principal components (PCs) were selected to replace the complex spectral data. The one hundred fifty samples were randomly separated into the calibration set and the validation set. Support vector machines (SVM) algorithm with four kinds of kernel functions was used to establish diagnosis models of the cucumber diseases and insect pests based on the calibration set, then these models were applied to the diagnosis of the validation set. According to the best diagnosis accuracy of cross-validation method in calibration set, the parameters of four kinds of kernel function models were optimized, and the capabilities of SVM with different kernel function were compared. Results showed that SVM with the ploy kernel function had the best identification capabilities and the accuracy was 98. 3% after the original spectrum noise was reduced by SG+FDT+ PCA. This research indicated that the method of PCA-SVM had a good identification effect and could realize rapid diagnosis of the cucumber diseases and insect pests as a new method.