Computers and Electronics in Agriculture最新文献

The yellowing morphologies of crops provide typical spectral characteristics for crop identification. However, this feature was generally neglected by most existing vegetation indices (VIs) focused on greening feature. Chinese GaoFen-6 satellite (GF-6) equipped with a wide field-of-view (WFV) camera has a refined spectral system within 0.40–0.89 μm, including the spectral bands that are sensitive to yellowing feature. This study proposes a new normalized difference yellow vegetation index (NDYVI) based on GF-6 image, by capitalizing on spectral reflectance feature of crops with yellowing morphologies, such as flowers and tassels. We used yellow and red-edge1 band to discriminate between crops within similar growing periods and incorporated NIR band to distinguish non-crop types. The performance of NDYVI was evaluated in two distinct classification scenarios involving different cropping systems: rapeseed with winter wheat in southern China, and maize with soybean in northeastern China. By calculating NDYVI and using Classification and Regression Tree (CART) algorithm, we generated classification maps in two scenarios. Additionally, the effectiveness of NDYVI was tested and compared with other six VIs, such as Normalized Difference Vegetation Index, Red-Edge Normalized Difference Vegetation Index and Normalized Difference Yellowness Index. The results demonstrated that NDYVI outperformed the other vegetation indices in both scenarios, achieving overall accuracies over 85 % (Kappa coefficient greater than 0.80) and each crop accuracy exceeding 80 %. Due to the higher reflectance in yellow band and red-edge1 band, NDYVI is more sensitive to canopies yellowness, which offers significant advantages in distinguishing crops during similar growing periods. Moreover, NDYVI is constructed from original spectral bands in GF-6 images, offering potential for significant flexibility in diverse classification scenarios. Consequently, NDYVI holds significant potential as a new vegetation index suitable for different remote sensing applications, including crop identification, growth monitoring and land cover classification.

The study of individual phenotypic information of cigar tobacco plants holds significant importance for enhancing mechanized production levels of cigar tobacco. It provides a foundational basis for the mechanization of field management, plant protection, and the design of harvesting machinery during the production process. Addressing the time-consuming, labour-intensive, inefficient, and highly subjective nature of traditional methods for extracting phenotypic information of cigar tobacco, this paper proposed a novel approach using terrestrial lidar scanning technology for the extraction of phenotypic information in field-grown cigar plants. By utilizing terrestrial lidar to acquire millimeter-precision three-dimensional point cloud data of individual cigar plants and conducting pre-processing of this point cloud data, the study employed a skeleton extraction algorithm based on Laplacian mesh contraction and topological refinement to construct a triangular mesh model of the leaves and a point cloud skeleton of the plant. Based on the triangular mesh of the leaves, this study extracted the leaf area, while the leaf length, leaf inclination angle, and petiole angle were derived from the plant’s skeletal point cloud. Additionally, the plant height was ascertained from the point cloud of the cigar tobacco plant. The experimental results, compared with manual field measurements, indicated that the Root Mean Square Error values for actual leaf length, leaf area, leaf inclination angle, petiole angle, and growth height were 1.659 cm, 8.374 cm², 2.371°, 2.73°, and 2.229 cm, respectively. The average absolute percentage errors for these measurements were 3.102 %, 0.782 %, 3.323 %, 4.148 %, and 1.194 %, respectively. This method provided an effective means of phenotypic information measurement to assist in the growth monitoring of mature cigar plants, mechanized plant protection, mechanized harvesting, and other projects that integrate agro-mechanics and agronomy.

The manual injection of therapeutic materials to trees is laborious and time-consuming, posing implementation challenges for commercial citrus growers. Compared to manual injection, a previously developed automated trunk injection mechanism reduced the labor required and the size of the injection port. The injection system has now been field-tested on HLB-infected Glen Navel orange trees grafted onto ’Swingle’ citrumelo rootstock while applying oxytetracycline hydrochloride (OTC) and water. The multi-puncture drill-free injection mechanism creates injection ports using a needle attached to an end effector and delivers the required volume of liquids using a metering pump. Injection parameters, including injection duration, pressure, and flow rate, were examined while performing injections, and a comparison was made with a manual trunk injection device commonly used for OTC injection of HLB-affected citrus trees. Maximum pressure was not significantly different between the automated and manual injection systems and for the different liquids injected (100 ml of 5500 ppm OTC, 11000 ppm OTC, and deionized water) as 0.1735 ± 0.054 MPa. However, the automated system had a significantly higher flow rate, decreasing injection duration by 81 times compared to the manual injection. Modifications and improvements to the previously developed automated injection system are also described, including the development of a clog-free needle, automation of the positioning arm, and a control system capable of delivering precise volume to each injection port on both sides of a tree trunk.

Accurately and automatically estimating vineyard yield is a significant challenge. This study focuses on grape bunch counting in commercial vineyards using advanced deep learning techniques and object detection algorithms. The aim is to overcome the limitations of conventional yield estimation techniques, which are labour intensive, costly, and often inaccurate due to the spatial and temporal variability of the vineyard. This research proposes a non-invasive methodology for identifying grape bunches under different occlusion conditions using RGB cameras and deep learning models. The methodology is based on the collection of RGB images captured under field conditions, coupled with the implementation of the YOLOv4 architecture for data processing and analysis. Statistical indicators were used to evaluate the performance of the developed models. The comprehensive model produced a favourable outcome during validation, with an error rate of 1.12 bunches (R² = 0.83). In the test dataset, the model achieved an error rate of 1.12 (R² = 0.81). The results highlight the potential of emerging technologies to significantly improve vineyard yield estimation. This approach has the potential to assist vineyard management practices, enabling more informed and efficient decisions that could increase both the quantity and quality of grape production intended for winemaking.

Knowledge graph technology is of great significance to modern agricultural information management and data-driven decision support. However, agricultural knowledge is rich in types, and agricultural knowledge graph databases built only based on text are not conducive to users’ intuitive perception and comprehensive understanding of knowledge. In view of this, this paper proposes a solution to extract knowledge and construct an agricultural multimodal knowledge graph using a pre-trained language model. This paper takes two plants, cabbage and corn, as research objects. First, a text-image collaborative representation learning method with a two-stream structure is adopted to combine the image modal information of vegetables with the text modal information, and the correlation and complementarity between the two types of information are used to achieve entity alignment. In addition, in order to solve the problem of high similarity of vegetable entities in small categories, a cross-modal fine-grained contrastive learning method is introduced, and the problem of insufficient semantic association between modalities is solved by contrastive learning of vocabulary and small areas of images. Finally, a visual multimodal knowledge graph user interface is constructed using the results of image and text matching. Experimental results show that the image and text matching efficiency of the fine-tuned pre-trained model on the vegetable dataset is 76.7%, and appropriate images can be matched for text entities. The constructed visual multimodal knowledge graph database allows users to query and filter knowledge according to their needs, providing assistance for subsequent research on various applications in specific fields such as multimodal agricultural intelligent question and answer, crop pest and disease identification, and agricultural product recommendations.

Making agricultural research datasets Findable, Accessible, Interoperable, and Reusable (FAIR) is an evolving priority for research organisations in Australia. Indigenous data governance standards, described in the CARE (Collective benefit, Authority to control, Responsibility and Ethics) principles complement FAIR principles when managing research datasets. Agricultural research data have traditionally been difficult to publicly access and share due in part to conflicting interests in ownership, commerce, multiparty contracts, and diverse research practices.

As part of an agriculture digital research platform development project (AgReFed Platform project), we develop here a workflow that applies the FAIR data and CARE principles to the Australian Drought Monitor dataset, a product developed as part of the Northern Australia Climate Program (NACP), a joint project funded by Meat and Livestock Australia, the Queensland Drought and Climate Adaptation Program and the University of Southern Queensland (UniSQ). We present here a complete process on how to apply the FAIR principles to the Australian Drought Monitor dataset, including a digital infrastructure development to enable its re-use in the AgReFed Platform project.

Digital cultivation is emerging as one of the most promising fields that helped in creating an ecosystem for smart farming. Precision farming, modernized techniques, and creating smart agriculture supply chains are the need of the hour for high-quality yield. Artificial Intelligence (AI) helps create a framework and plays an important role in making decisions by analysing various data points. There are countries where more than 70% of the population depends on agriculture for their living, technological advancements help to improve crop yields and get better farming results through sustainable ways. Each stage in agriculture, starting from preparation of land, crop selection, type of fertilizer to use, to the kind of watering needed for the crops; can be monitored and regulated by technological advancement. Farmers can also make decisions and implement the best practices in their field by using AI and allied technologies. Disruptive technologies such as blockchain, the Internet of Things, remote sensing, imaging technologies, and drones can transform the primitive way of agriculture. Market analysis and user demands can also be foreseen, which helps farmers to get better yields. Another important sector where technology can play a big role in disease control and pest management. Artificial Intelligence-based farming creates high productivity and better yield, increasing individual farmers’ profit. In this study, the authors would like to throw light on AI and allied technologies, which can make agricultural productivity increase significantly. In a post-pandemic situation, high-yield and more productive farming will have a major impact. An agricultural intelligence framework model for self-sustained farming is proposed in this work. The proposed framework will help achieve self-sustained growth with increased economic stability. An end-to-end supply chain ensures customers are provided with quality products and farmers are not financially looted. Technology-driven farming will also push the next generation to take up agricultural jobs. The various advancements and strategies we propose in this study aim to build a better ecosystem for transforming Artificial Intelligence into agricultural intelligence.

The cow digital shadow reflects the behavior, health condition, and productivity of cows, playing a crucial role in ensuring animal welfare, increasing individual productivity, and improving breeding efficiency. To fully utilize the existing multimodal data on farms and build a cow digital shadow with rich behavioral information, this study proposes a multimodal data fusion algorithm for recognizing cow behaviors such as drinking, feeding, lying, standing, and walking. This algorithm leverages the strengths of different data modalities, complementing each other, and enhances the performance of the cow behavior classification model. The algorithm integrates motion sensor and video data, collected by custom-made collars with inertial measurement units (IMUs) sensors placed at the top of the cow’s neck and cameras in the barn, using EfficientNet V2 S, BiLSTM, and Transformer networks. Experimental results demonstrate recognition accuracies of 98.80 %, precision of 97.15 %, and recall rates of 96.93 %, showing significant improvements over single-modal data behavior recognition algorithms. This method maximizes the utility of existing multimodal data to generate a cow digital shadow with detailed behavioral information, enhancing the modeling and simulation element of the cow digital twin architecture and laying the foundation for developing a comprehensive cow behavior data model.

Livestock is an essential source of livelihood and food. In the context of climate change, animal-based greenhouse gas (GHG) emissions are of great importance. This study predicted direct N₂O Emissions, indirect N₂O Emissions, CH₄ Emissions from manure management, CH₄ Emissions from enteric fermentation, and CO₂ Emissions as GHG emissions from animal sources for all provinces of Turkey using various machine learning algorithms. Animal populations, climate parameters, and agricultural area information are used to model GHG emissions. The proposed study includes two different analyses according to the number of features used. The CatBoost algorithm was primarily successful when using eight features according to Scenario-1 and twelve features according to Scenario-2. In Scenario-1, R² values for GHG emission predictions for 2021 are obtained as 0.996, 0.996, 0.992, 0.999, and 0.996, respectively, while in Scenario-2, they are obtained as 0.995, 0.996, 0.984, 0.996, and 0.996. In Scenario-1, R² values for GHG emission predictions for 2004–2009 are obtained as 0.976, 0.962, 0.982, 0.994, and 0.994, respectively, while in Scenario-2, they are obtained as 0.975, 0.957, 0.917, 0.993, and 0.993. According to the results, the model’s performance was not improved by increasing the number of features used. Using fewer features gave more successful results.

Precision fertilizing is crucial not only for enhancing fertilizer efficiency but also for protecting the environment. The rapid sensing of total soil nitrogen (TN) constitutes a key aspect of precision fertilization. Currently common methods, such as the Kjeldahl method, are not suitable for on-site applications. Laser-induced breakdown spectroscopy (LIBS), celebrated for its expeditious data acquisition and high precision, has seen widespread deployment in rapid soil sensing. However, the time-consuming sample preprocessing stage restricts the on-site application of LIBS. In this study, we employed a powder adhesion (PA) method to shorten the preprocessing cycle to 3 min. A transfer learning approach named TransLIBS is introduced to ensure the estimation performance of PA. Compared to the calibration model directly developed on the target domain, the transferred model by TransLIBS elevates $R_V^2$ by 0.134 and diminishes ${\mathrm{RMSE}}_V$ by 0.312 g kg⁻¹. The F-test method is leveraged to identify active variables, and feature map visualization is employed to interpret the transfer mechanism of the TransLIBS approach. The visualization results highlight the most influential variables situated in the 212–310 nm and 391–395 nm range. Transfer learning has advanced the application of LIBS in soil, providing more opportunities for on-site LIBS detection.