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Computers and Electronics in Agriculture
Elsevier Science Publishers
Computers and Electronics in Agriculture

Elsevier Science Publishers

0168-1699

Computers and Electronics in Agriculture/Journal Computers and Electronics in AgricultureSCIEIISTP
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    NIR spectroscopy and artificial neural network for seaweed protein content assessment in-situ

    Tadmor Shalev N.Ghermandi A.Tchernov D.Shemesh E....
    14页
    查看更多>>摘要：? 2022 Elsevier B.V.Determining seaweed protein concentration and the associated phenotype is critical for food industries that require precise tools to moderate concentration fluctuations and attenuate risks. Algal protein extraction and profiling have been widely investigated, but content determination involves a costly, time-consuming and high-energy, laboratory-based fractionation technique. The present study examines the potential of a field spectroscopy technology as a precise, non-destructive tool for on-site detection of red seaweed protein concentration. By using information from a large dataset of 144 Gracilaria sp. specimens, studied in a land-based cultivation set-up, under six treatment regimes during two cultivation seasons, and an artificial neural network, machine learning algorithm and diffuse visible–near infrared reflectance spectroscopy, predicted protein concentrations in the algae were obtained. The prediction results were highly accurate (R2 = 0.95; RMSE = 0.84), exhibiting a high correlation with the analytically determined values. External validation of the model derived from a separate trial, exhibited even better results (R2 = 0.99; RMSE = 0.45). This model, trained to convert phenotypic spectral measurements and pigment intensity into accurate protein content predictions, can be adapted to include diversified algae species and usages.
      原文链接:
    • NSTL
    • Elsevier

    Corn seedling recognition algorithm based on hyperspectral image and lightweight-3D-CNN

    Diao Z.Yan J.He Z.Zhao S....
    9页
    查看更多>>摘要：? 2022 Elsevier B.V.Weeds can severely harm corn seedlings. If weeds are not removed promptly, a series of problems arise, eventually resulting in a decrease in crop yield. In this study, corn seedlings and weeds were used as the research object to achieve rapid training and recognition of corn seedlings and weeds in hyperspectral images, a lightweight three-dimensional convolutional neural network (lightweight-3D-CNN) model was proposed, and two lightweight units were designed in the network. First, an improved band selection network based on fully connected networks (improved BS-Net-FC) was used to screen out characteristic bands to reduce the number of input channels of the model. Second, the training samples were rotated and flipped horizontally in the plane space, and the training samples were expanded six times. Then, the spectral data of the selected 15 optimal bands were tested on the lightweight-3D-CNN, lightweight-2D-CNN, 3D-CNN, and 2D-CNN models. Finally, the computational efficiency of the full band was compared with that of the optimal band. The test results showed that the average recognition accuracy of the model proposed in this study was 98.58%, which was improved by 1.30%, 3.23%, and 6.68% when compared with the lightweight-2D-CNN, 3D-CNN, and 2D-CNN models, respectively. In terms of computational efficiency, although the accuracy drops slightly in the optimal band, the trainable parameters are nearly 10 times less than those in the full band, and the training time, as well as the testing time, are significantly reduced. It provides a feasible technical approach for the fast training and identifying hyperspectral images of corn seedlings and weeds.
      原文链接:
    • NSTL
    • Elsevier

    Smart fuzzy irrigation system for litchi orchards

    Xie J.Chen Y.Gao P.Sun D....
    11页
    查看更多>>摘要：? 2022Sustainability of orchard crop production can be improved by developing more efficient irrigation control systems. Soil moisture deficiency can lead to yield reduction; however, excess soil moisture can reduce the diffusion of oxygen to the root system, which can result in hypoxia that causes harmful results. The particle swarm optimization (PSO) algorithm can fall into the local optimal solution, and therefore, it requires further optimization. In addition, a mathematical model that can effectively describe the system is difficult to obtain in complex systems with nonlinear characteristics, such as in irrigation systems. Therefore, a smart irrigation fuzzy control system based on an improved PSO algorithm is proposed in this study. Simulation and field experiments were conducted to analyze the effectiveness of the system. The simulation results showed that the proposed irrigation control algorithm achieved better transient performance and control precision. Further, the time required to enter the steady state and the overshoot were reduced by 40% and 76%, respectively, compared to the values for general fuzzy control. The experimental results showed that the irrigation system proposed in this paper can increase the average soil moisture of litchi orchards to 16.43% with an average deviation of 0.00826. The general fuzzy irrigation system achieved an average soil moisture of 16.83% with an average deviation of 0.01107, which implies the proposed irrigation system's good control performance. The results indicate that the system is more efficient for making the soil moisture suitable for litchi growth. This research was meaningful with regards to controlling the soil moisture stably and thereby providing a valuable reference for the litchi orchard's irrigation.
      原文链接:
    • NSTL
    • Elsevier

    Optimizing the path of seedling transplanting with multi-end effectors by using an improved greedy annealing algorithm

    Qiu Z.Zhou H.Yu G.Wu C....
    15页
    查看更多>>摘要：? 2022 Elsevier B.V.Automated transplanters perform the repetitive task of replugging bad or missing cells with healthy seedlings in greenhouses. The work efficiency of transplanters can be improved by optimizing the transplanting paths of manipulators. In this study, an improved greedy simulated annealing algorithm (IGSA) based on MTSP model was proposed for path optimization. Aimed at improving the computation speed of the algorithm, a new improved 2-Opt algorithm based on the metropolis criterion was utilized. Moreover, the greedy algorithm (GRA) and the greedy genetic algorithm (GGA) were modified to adapt to the multi-end effectors’ replugging operation. The performance of IGSA and those of the GGA, GRA, and common sequence method (CSM) in path planning for replugging transplanting were compared in terms of their optimization effects and computation times. Then, manipulators with different numbers of end effectors (four, five, and six end effectors) were used to verify the increased effect of the path planning. Under the verification test conditions, the comparative average optimization ratios of GRA, GGA, and IGSA compared to the CSM were 4.87 %, 10.64 %, and 20.07 %, respectively. The performance ranking of the methods with short average paths was in the order of IGSA, GGA, GRA, and CSM. Compared with the average path shortening ratio of the four end effectors, those of the five and six end effectors were 11.44 % and 18.68 %, respectively. The average computation times of CSM, GRA, GGA, and IGSA were 0.002, 0.007, 6.94, and 3.49 s in MATLAB (R2019a), respectively. All of them can meet the real-time operation requirements except for GGA. In contrast to GGA, GRA, and CSM, the proposed IGGA performed effectively in the path planning of the seedlings’ replug transplanting owing to its comprehensive performance derived from its path optimization ratio and low cost in terms of computation time.
      原文链接:
    • NSTL
    • Elsevier

    Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

    Yu L.Bu K.Zheng X.Zhang S....
    14页
    查看更多>>摘要：? 2022 Elsevier B.V.Northeast China is an important commodity grain base in China and plays an important role in maintaining national and local food security. The intensifying climate variability and strengthening of extreme weather events threaten crop growth and yields in this region. The dynamic crop model coupled into the land surface models showed advances in simulating crop phenology and crop growth response to climate change, however, which was mainly validated for the United States. By comparing with the satellite observed crop growth cycles, we firstly calibrated the parameters for the Weather Research and Forecasting (WRF) coupled Noah-MP-Crop model. Then, we evaluated the performance of the calibrated WRF-Noah-MP-Crop model with the default crop models and the dynamic vegetation models coupled with WRF by comparing our simulations with corresponding satellite measurements and meteorological observations. Our results indicated the augmentation of the Noah-MP-Crop model in WRF significantly improved the crop phenology simulation at the beginning and ending time of the growing season compared with the dynamic vegetation model using unmanaged crops. The satellite-calibrated crop parameters substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%. The coupled WRF-Noah-MP-Crop model with flexible resolutions holds unmatched advantages in estimating and projecting crop growth and yield under future climate change scenarios, which would be critical in ensuring sustainable agricultural development and maintaining future food security.
      原文链接:
    • NSTL
    • Elsevier

    Computer vision-based platform for apple leaves segmentation in field conditions to support digital phenotyping

    Uryasheva A.Kalashnikova A.Evteeva K.Moskovtsev E....
    11页
    查看更多>>摘要：? 2022Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution.
      原文链接:
    • NSTL
    • Elsevier

    Winter wheat and soil total nitrogen integrated monitoring based on canopy hyperspectral feature selection and fusion

    Tian Z.Zhang Y.Zhang H.Li M....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Nitrogen, as a key element for crop reproductive and nutritional growth, plays an important role in overall agricultural production. Hyperspectral remote sensing (RS) offers possibility for monitoring farm nitrogen content by its non-destructive and high throughput advantages. However, the integrated high-precision acquisition of nitrogen content in crops and soils is still a technical challenge that needs to be solved urgently. This study proposed a novel approach combining feature selection based on improved grey wolf optimizer (IGWO) and feature fusion with projected gradient nonnegative matrix factorization and matrix cross recombination (PNMF-MCR) to realize the integrated monitoring of total nitrogen (TN) content in winter wheat and soil based on crop canopy hyperspectral data. Therein, the IGWO originated from GWO, additionally considered a comprehensive feature selection criterion which are the relevance with TN, the representative ability of the entire spectra, and the redundancy of the selected wavebands. The selected TN sensitive wavebands are 487, 611, 706, 817, 920 nm for winter wheat and 478, 587, 699, 812, 892 nm for soil, which could effectively represent the TN information of full spectrum from the aspects of nitrogen metabolism enzymes, chlorophyll photosynthesis, and the structure of cellular arrangements within the leaf. To further integrate the TN sensitive spectral information of crop and soil, PNMF-MCR was firstly proposed to generate a feature matrix VF by fusing the winter wheat and soil TN sensitive wavelengths. The TN inversion results based on the fused feature matrix VF achieved a high accuracy for both winter wheat and soil simultaneously. On the ground-based RS platform, the R2 and RMSE of the validation dataset for winter wheat are 0.7920 and 0.1431 g/kg. The validation results for soil are 0.7928 and 0.0037 g/kg. On the Airborne-based RS platform, the R2 and RMSE of the validation dataset for winter wheat are 0.6934 and 0.1490 g/kg. The validation results for soil are 0.6988 and 0.0040 g/kg. To verify the proposed method, predictive performances for TN of winter wheat and soil were validated in different time, at different locations for different winter wheat varieties on different platforms (ground-based platform and airborne-based platform). All above validations demonstrated the good universality and predictive ability of proposed hybrid method, which provide a feasible solution for integrated monitoring of crop and soil.
      原文链接:
    • NSTL
    • Elsevier

    Study on hyperspectral monitoring model of soil total nitrogen content based on fractional-order derivative

    Yang C.Feng M.Song L.Jing B....
    11页
    查看更多>>摘要：? 2022 Elsevier B.V.Realizing the real-time nondestructive monitoring of soil total nitrogen (STN) content is an important task to promote precision agriculture development. In this study, a water regulation experiment was performed. Winter wheat and summer soybean soils were taken as the research objects. The effects of fractional-order derivatives (FOD) on soil spectral reflectance were studied, and the hyperspectral monitoring models of STN content were constructed based on the full spectrum band and the uninformative variable elimination (UVE) -extracted band. The main results were as follows: The FOD showed a gradual change in the integer-order derivative (IOD). When calculating the FOD, the more fitting points there were, the smaller the FOD spectrum, and the higher the correlation with the STN content. In the range of orders 0–1, the correlation increased with increasing order. In the range of orders 1–2, the correlation first decreased and then increased. When constructing the model, stepwise multiple linear regression (SMLR) was used, but the accuracies of the SMLR models were low. Afterward, the bands were extracted by UVE, and the models were constructed by partial least squares regression (PLSR). The number of bands extracted by UVE ranged from 11 to 534, mainly in the range of 1000–2450 nm. Under the same conditions, the accuracy of the PLSR model constructed with the UVE-extracted bands was higher than that of the model constructed with the full band spectrum. Among all PLSR models, when the number of fitting points, the order, and the number of bands were 40, 0.67, and 272, respectively, the model accuracy was the highest. The coefficient of determination of cross-validation (R2cv), root mean square error of cross-validation (RMSEcv), coefficient of determination of validation (R2v), root mean square error of validation (RMSEv), and relative analysis error of validation (RPDv) of the model were 0.7317, 0.2244 g·kg?1, 0.7937, 0.1976 g·kg?1, and 2.1904, respectively. This model could realize the hyperspectral monitoring of STN content in wheat and soybean fields. In this study, hyperspectral spectroscopy was used to monitor the STN content, which provided a theoretical basis and technical support for the observations.
      原文链接:
    • NSTL
    • Elsevier

    Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network

    Kang X.Huang C.Zhang L.Zhang Z....
    17页
    查看更多>>摘要：? 2022 The AuthorsAs the largest cotton-growing region in China, Xinjiang has contributed more than 80% of the total national cotton production in recent years. Timely and accurate estimation of cotton yield in Xinjiang is important for sustainable agricultural development and food security. However, most current studies have been devoted to the linkage of crop yield with remotely sensed reflectance and climate parameters. This has caused numerous uncertainties due to that these explanatory variables are unable to quickly reflect the actual photosynthetic dynamics of crops. Solar-induced chlorophyll fluorescence (SIF), as a direct proxy of plant photosynthesis (gross primary productivity, GPP), has recently been suggested to be a promising method for crop yield estimation, but the spatial resolution of current SIF products derived from satellites is usually very low (such as Global OCO-2 SIF (GOSIF): 0.05°). This greatly limited the ability of SIF to accurately estimate field-scale cotton yield in Xinjiang. Here, we first proposed a two-step convolutional neural network (CNN) strategy to downscale the monthly GOSIF products sequentially from 0.05°, 0.005° to 0.0005° to match the size of cotton field parcels, and then linear regression and random forest (RF) regression were respectively conducted using the monthly downscaled SIF product (CNN-SIF) to assess its feasibility to estimate field-scale yield. Results showed that the proposed stepwise approach for downscaling GOSIF worked well, indicating a high goodness of fit (R2 > 0.85) with the referenced SIF as well as strong correlations to both GPP products and fraction of photosynthetically active radiation (FPAR) (the median r > 0.90). On this basis, preferable accuracies (the optimal R2 = 0.62 and the ratio of prediction to deviation = 1.64) were also achieved for our proposed cotton yield estimation models in the Mosuowan region, Xinjiang only by the 0.0005° SIF products. With the assistance of NDVI (normalized difference vegetation index), the higher performance was given (R2 = 0.67 and RPD = 1.72). This study reveals the importance of finer-resolution SIF products for accurate crop yield estimation and offers a promising and practical approach for estimating agricultural yield, especially for fragmented farmlands.
      原文链接:
    • NSTL
    • Elsevier

    Design of feed rate monitoring system and estimation method for yield distribution information on combine harvester

    Sun Y.Liu R.Zhang M.Li M....
    9页
    查看更多>>摘要：? 2022 Elsevier B.V.Real-time monitoring of harvester feed rate is of great significance for guiding harvesting work and improving harvesting efficiency. In this study, a feed rate monitoring system is designed and developed. The system consists of a torque sensor for measuring the header power shaft torque, an angel sensor for measuring header height, a humidity sensor for measuring grains moisture content, a Global Navigation Satellite System receiver, and a vehicle industrial computer. Data, such as the moisture content of grains and the working position of the harvester, are collected by the system during harvest. The height of the header, the moisture content of grains, and the torque of the header power shaft are selected as the input quantity, and feed rate is selected as the output quantity. A calculation model for feed rate is established using a particle swarm optimization–back propagation neural network, and then real-time monitoring of feed rate is realized. On the basis of the monitoring of feed rate, an estimation method for yield distribution information is proposed. Through analyzing the rice samples in the experimental area, the method establishes a binary linear model between the header height, the grain moisture content, and the stem-to-grain ratio of the rice fed to the harvester. The model is combined with the monitoring results of feed rate to obtain the rice yield data fed to the harvester. Then, the rice yield data are matched with the positional information at the time of harvesting to obtain a yield distribution map of the harvested area. Field experiments are conducted in a rice-growing area in Northeast China, and the average relative error of the feed rate monitoring system is 7.69%. The field sample verification accuracy of the stem-to-grain ratio model is higher than 90%, and the correlation coefficient R2 is 0.93. The estimation accuracy of the yield distribution is higher than 90%, which provides a scientific basis for agricultural decision making.
      原文链接:
    • NSTL
    • Elsevier