Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Basic Disciplines of Agriculture submitted time 2023-08-14 Cooperative journals: 《智慧农业（中英文）》
Abstract： [Significance] Agricultural environment is dynamic and variable, with numerous factors affecting the growth of animals and plants and complex interactions. There are numerous factors that affect the growth of all kinds of animals and plants. There is a close but complex correlation between these factors such as air temperature, air humidity, illumination, soil temperature, soil humidity, diseases, pests, weeds and etc. Thus, farmers need agricultural knowledge to solve production problems. With the rapid development of internet technology, a vast amount of agricultural information and knowledge is available on the internet. However, due to the lack of effective organization, the utilization rate of these agricultural information knowledge is relatively low.How to analyze and generate production knowledge or decision cases from scattered and disordered information is a big challenge all over the world. Agricultural knowledge intelligent service technology is a good way to resolve the agricultural data problems such as low rank, low correlation, and poor interpretability of reasoning. It is also the key technology to improving the comprehensive prediction and decision-making analysis capabil‐ities of the entire agricultural production process. It can eliminate the information barriers between agricultural knowledge, farmers, and consumers, and is more conducive to improve the production and quality of agricultural products, provide effective information services. [Progress] The definition, scope, and technical application of agricultural knowledge intelligence services are introduced in this paper. The demand for agricultural knowledge services are analyzed combining with artificial intelligence technology. Agricultural knowledge intelligent service technologies such as perceptual recognition, knowledge coupling, and inference decision-making are conducted. The characteristics of agricultural knowledge services are analyzed and summarized from multiple perspectives such as industrial demand, industrial upgrading, and technological development. The development history of agricultural knowledge services is introduced. Current problems and future trends are also discussed in the agricultural knowledge services field. Key issues in agricultural knowledge intelligence services such as animal and plant state recognition in complex and uncertain environments, multimodal data association knowledge extraction, and collaborative reasoning in multiple agricultural application scenarios have been discussed. Combining practical experience and theoretical research, a set of intelligent agricultural situation analysis service framework that covers the entire life cycle of agricultural animals and plants and combines knowledge cases is proposed. An agricultural situation perception framework has been built based on satellite air ground multi-channel perception platform and Internet real-time data. Multimodal knowledge coupling, multimodal knowledge graph construction and natural language processing technology have been used to converge and manage agricultural big data. Through knowledge reasoning decision-making, agricultural information mining and early warning have been carried out to provide users with multi-scenario agricultural knowledge services. Intelligent agricultural knowledge services have been designed such as multimodal fusion feature extraction, cross domain knowledge unified representation and graph construction, and complex and uncertain agricultural reasoning and decision-making. An agricultural knowledge intelligent service platform composed of cloud computing support environment, big data processing framework, knowledge organization management tools, and knowledge service application scenarios has been built. Rapid assembly and configuration management of agricultural knowledge services could be provide by the platform. The application threshold of artificial intelligence technology in agricultural knowledge services could be reduced. In this case, problems of agricultural users can be solved. A novel method for agricultural situation analysis and production decision-making is proposed. A full chain of intelligent knowledge application scenario is constructed. The scenarios include planning, management, harvest and operations during the agricultural before, during and after the whole process. [Conclusions and Prospects] The technology trend of agricultural knowledge intelligent service is summarized in five aspects. (1) Multi-scale sparse feature discovery and spatiotemporal situation recognition of agricultural conditions. The application effects of small sample migration discovery and target tracking in uncertain agricultural information acquisition and situation recognition are discussed. (2) The construction and self-evolution of agricultural cross media knowledge graph, which uses robust knowledge base and knowledge graph to analyze and gather high-level semantic information of cross media content. (3) In response to the difficulties in tracing the origin of complex agricultural conditions and the low accuracy of comprehensive prediction, multi granularity correlation and multi-mode collaborative inversion prediction of complex agricultural conditions is discussed. (4) The large language model (LLM) in the agricultural field based on generative artificial intelligence. ChatGPT and other LLMs can accurately mine agricultural data and automatically generate questions through large-scale computing power, solving the problems of user intention understanding and precise service under conditions of dispersed agricultural data, multi-source heterogeneity, high noise, low information density, and strong uncertainty. In addition, the agricultural LLM can also significantly improve the accuracy of intelligent algorithms such as identification, prediction and decision-making by combining strong algorithms with Big data and super computing power. These could bring important opportunities for large-scale intelligent agricultural production. (5) The construction of knowledge intelligence service platforms and new paradigm of knowledge service, integrating and innovating a self-evolving agricultural knowledge intelligence service cloud platform. Agricultural knowledge intelligent service technology will enhance the control ability of the whole agricultural production chain. It plays a technical support role in achieving the transformation of agricultural production from "observing the sky and working" to "knowing the sky and working". The intelligent agricultural application model of "knowledge empowerment" provides strong support for improving the quality and efficiency of the agricultural industry, as well as for the modernization transformation and upgrading.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： As one of the important way for constructing smart agriculture, unmanned farms are the most attractive in nowadays, and have been explored in many countries. Generally, data, knowledge and intelligent equipment are the core elements of unmanned farms. It deeply integrates modern information technologies such as the Internet of Things, big data, cloud computing, edge computing, and artificial intelligence with agriculture to realize agricultural production information perception, quantitative decision-making, intelligent control, precise input and personalized services. In the paper, the overall technical architecture of unmanned farms is introduced, and five kinds of key technologies of unmanned farms are proposed, which include information perception and intelligent decision-making technology, precision control technology and key equipment for agriculture, automatic driving technology in agriculture, unmanned operation agricultural equipment, management and remote controlling system for unmanned farms. Furthermore, the latest research progress of the above technologies both worldwide are analyzed. Based on which, critical scientific and technological issues to be solved for developing unmanned farms in China are proposed, include unstructured environment perception of farmland, automatic drive for agriculture machinery in complex and changeable farmland environment, autonomous task assignment and path planning of unmanned agricultural machinery, autonomous cooperative operation control of unmanned agricultural machinery group. Those technologies are challenging and absolutely, and would be the most competitive commanding height in the future. The maize unmanned farm constructed in the city of Gongzhuling, Jilin province, China, was also introduced in detail. The unmanned farms is mainly composed of information perception system, unmanned agricultural equipment, management and controlling system. The perception system obtains and provides the farmland information, maize growth, pest and disease information of the farm. The unmanned agricultural machineries could complete the whole process of the maize mechanization under unattended conditions. The management and controlling system includes the basic GIS, remote controlling subsystem, precision operation management subsystem and working display system for unmanned agricultural machineries. The application of the maize unmanned farm has improved maize production efficiency (the harvesting efficiency has been increased by 3－4 times) and reduced labors. Finally, the paper summarizes the important role of the unmanned farm technology were summarized in solving the problems such as reduction of labors, analyzes the opportunities and challenges of developing unmanned farms in China, and put forward the strategic goals and ideas of developing unmanned farm in China.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic analysis of wheat plants, three-dimensional (3D) digitization was used to extract phenotypic parameters of wheat plants. Firstly, digital representation method of wheat organs was given and a 3D digital data acquisition standard suitable for the whole growth period of wheat was formulated. According to this standard, data acquisition was carried out using a 3D digitizer. Based on the definition of phenotypic parameters and semantic coordinates information contained in the 3D digitizing data, eleven conventional measurable phenotypic parameters in three categories were quantitative extracted, including lengths, thicknesses, and angles of wheat plants and organs. Furthermore, two types of new parameters for shoot architecture and 3D leaf shape were defined. Plant girth was defined to quantitatively describe the looseness or compactness by fitting 3D discrete coordinates based on the least square method. For leaf shape, wheat leaf curling and twisting were defined and quantified according to the direction change of leaf surface normal vector. Three wheat cultivars including FK13, XN979, and JM44 at three stages (rising stage, jointing stage, and heading stage) were used for method validation. The Open3D library was used to process and visualize wheat plant data. Visualization results showed that the acquired 3D digitization data of maize plants were realistic, and the data acquisition approach was capable to present morphological differences among different cultivars and growth stages. Validation results showed that the errors of stem length, leaf length, stem thickness, stem and leaf angle were relatively small. The R2 were 0.93, 0.98, 0.93, and 0.85, respectively. The error of the leaf width and leaf inclination angle were also satisfactory, the R2 were 0.75 and 0.73. Because wheat leaves are narrow and easy to curl, and some of the leaves have a large degree of bending, the error of leaf width and leaf angle were relatively larger than other parameters. The data acquisition procedure was rather time-consuming, while the data processing was quite efficient. It took around 133 ms to extract all mentioned parameters for a wheat plant containing 7 tillers and total 27 leaves. The proposed method could achieve convenient and accurate extraction of wheat phenotypes at individual plant and organ levels, and provide technical support for wheat shoot architecture related research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Raman spectroscopy is a type of scattering spectroscopy with features such as rapid, less susceptible to moisture interference, no sample pre-treatment and in vivo detection. As a powerful characterization tool for analyzing and testing the molecular composition and structure of substances, Raman spectroscopy is also playing an extremely important role in the detection of plant and animal phenotypes, food safety, soil and water quality in the agricultural field with the continuous improvement of Raman spectroscopy technology. In this paper, the detection principles of Raman spectroscopy are introduced, and the new progresses of eight Raman spectroscopy technology are summarized, including confocal microscopy Raman spectroscopy, Fourier transform Raman spectroscopy, surface-enhanced Raman spectroscopy, tip-enhanced Raman spectroscopy, resonance Raman spectroscopy, spatially shifted Raman spectroscopy, frequency-shifted excitation Raman difference spectroscopy and Raman spectroscopy based on nonlinear optics, etc. And their advantages and disadvantages and application scenarios are prerented, respectively. The applications of Raman spectroscopy in plant detection, soil detection, water quality detection, food detection, etc. are summarized. It can be specifically subdivided into plant phenotype, plant stress, soil pesticide residue detection, soil colony detection, soil nutrient detection, food pesticide detection, food quality detection, food adulteration detection, and water quality detection. In future agricultural applications, the elimination of fluorescence background due to complex living organisms in Raman spectroscopy is the next research direction. The study of stable enhanced substrates is an important direction in the application of Surface Enhanced Raman Spectroscopy (SERS). In order to meet the measurement of different scenarios, portable and telemetric Raman spectrometers will also play an important role in the future. Raman spectroscopy needs to be further explored for a wide variety of research objects in agriculture, especially for applications in animal science, for which there is still a paucity of relevant studies up to now. In the existing field of agricultural research, it is necessary to pursue the characterization of more specific substances by Raman spectroscopy, which can prompt the application of Raman spectroscopy for a wider range of uses in agriculture. Further, the pursuit of lower detection limits and higher stability for practical applications is also the direction of development of Raman spectroscopy in the field of agriculture. Finally, the challenges that need to be solved and the future development directions of Raman spectroscopy are proposed in the field of agriculture in order to bring more inspiration to future agricultural production and research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Aiming at the real-time detection and classification of the growth period of crops in the current digital cultivation and regulation technology of facility agriculture, an improved YOLOv4 method for identifying the growth period of strawberries in a greenhouse environment was proposed. The attention mechanism into the Cross Stage Partial Residual (CSPRes) module of the YOLOv4 backbone network was introduced, and the target feature information of different growth periods of strawberries while reducing the interference of complex backgrounds was integrated, the detection accuracy while ensured real-time detection efficiency was improved. Took the smart facility strawberry in Yunnan province as the test object, the results showed that the detection accuracy (AP) of the YOLOv4-CBAM model during flowering, fruit expansion, green and mature period were 92.38%, 82.45%, 68.01% and 92.31%, respectively, the mean average precision (mAP) was 83.78%, the mean inetersection over union (mIoU) was 77.88%, and the detection time for a single image was 26.13 ms. Compared with the YOLOv4-SC model, mAP and mIoU were increased by 1.62% and 2.73%, respectively. Compared with the YOLOv4-SE model, mAP and mIOU increased by 4.81% and 3.46%, respectively. Compared with the YOLOv4 model, mAP and mIOU increased by 8.69% and 5.53%, respectively. As the attention mechanism was added to the improved YOLOv4 model, the amount of parameters increased, but the detection time of improved YOLOv4 models only slightly increased. At the same time, the number of fruit expansion period recognized by YOLOv4 was less than that of YOLOv4-CBAM, YOLOv4-SC and YOLOv4-SE, because the color characteristics of fruit expansion period were similar to those of leaf background, which made YOLOv4 recognition susceptible to leaf background interference, and added attention mechanism could reduce background information interference. YOLOv4- CBAM had higher confidence and number of identifications in identifying strawberry growth stages than YOLOv4-SC, YOLOv4-SE and YOLOv4 models, indicated that YOLOv4-CBAM model can extract more comprehensive and rich features and focus more on identifying targets, thereby improved detection accuracy. YOLOv4-CBAM model can meet the demand for real-time detection of strawberry growth period status.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Irrigation is an important factor that affects crop yield. In order to control irrigation of facility crops more effectively and accurately, this study took "Zhangji" strawberry as an example, introduced crop real-time growth characteristics into irrigation decision-making, and combined Penman-Monteith (P-M) model and knowledge reasoning to study the irrigation of strawberry. In the first step, the influencing factors and expert experience in identifying strawberry growth period of "Zhangji" strawberry irrigation were standardized, and the strawberry irrigation data structure based on Resource Description Framework (RDF) was established. The second step was to collect expert experience of strawberry irrigation according to the standardized knowledge structure model. Firstly, all data were unified into structured data, and then were stored in *.csv format together with expert experience, and strawberry irrigation knowledge map based on Neo4j was constructed. The third step was to collect the environmental data and plant data of strawberry in each growth period. The fourth step was using P-M model to calculate the initial irrigation value of strawberry, and then adjusted the initial irrigation value by knowledge reasoning.The fifth step was to conduct experimental planting and evaluate the sampled fruits. In knowledge reasoning, irrigation adjustment strategies of each expert was different. In strawberry irrigation experiment based on P-M model and path sorting algorithm, a group of irrigation reasoning values with the highest probability value were selected to adjust irrigation with the goal of maximizing strawberry yield. The experimental results showed that under the condition of harvesting at a specified time, The total fruit yield, average fruit yield per plant and average fruit weight percentage increased by 2478.5 g, 20.65 g and 12.15% (average fruit weight increased by 1.65 g per fruit) based on P-M model and path sorting algorithm compared with traditional P-M model, respectively. First, on the basis of P-M model, the yield-first irrigation adjustment strategy was adopted. Based on knowledge reasoning, the irrigation frequency and amount were adjusted timely according to the crop growth situation, which improved the yield. Second, under the condition of harvesting and recording yield at a specified time, the experiment accurately controlled the growth period to ensure early fruit ripening, while the other three groups of fruits were not fully mature and the yield of immature fruits were not calculated. Under the method of strawberry irrigation based on Penman-Monteith model and path sorting algorithm, the fruit was picked within a fixed time and reached 0.39 kg/cm2, which increased by 0.1 kg/cm2. Because the planting goal of this study was yield first, only the influence of irrigation on yield was considered. The experimental resulted show that the irrigation method based on model and knowledge reasoning could improve the yield of strawberry, and can provide a new idea for precise irrigation.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Plant micro-phenotype mainly refers to the phenotypic information at the tissue, cell, and subcellular levels, which is an important part of plant phenomics research. In view of the problems of low efficiency, large error, and few traits of traditional methods for detecting kernel microscopic traits, Micro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels. A total of 34 microscopic traits were obtained based on CT sequence images of 7 tissues, including seed, embryo, endosperm, cavity, subcutaneous cavity, endosperm cavity and embryo cavity. Among the 34 microscopic traits, 4 traits, including endosperm cavity surface area, kernel volume, endosperm volume ratio and endosperm cavity specific surface area, were significantly different among maize types (P-value<0.05). The surface area of endosperm cavity and kernel volume of common maize were significantly higher than those of other types of maize. The specific surface area of endosperm cavity of high oil maize was the largest. The endosperm cavity of sweet corn had the smallest specific surface area. The endosperm volume ration of popcorn was the largest. Furthermore, 34 traits were used for One-way ANOVA and cluster analysis, and 11 different maize varieties were divided into four categories, of which the first category was mainly common maize, the second category was mainly popcorn, the third category was sweet corn, and the fourth category was high oil maize. The results indicated that Micro-CT scanning technology could not only achieve precise identification of micro-phenotype of maize kernels, but also provide supports for kernel classification and variety detection, and so on.
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