Computers and Electronics in Agriculture最新文献

Reliable soil moisture (SM) data is critical for effective water resources management, yet its accurate measurement and prediction remain challenging. This study was conducted to develop a deep learning regression network for sub-hourly SM prediction and compare its performance with traditional machine learning models, including the eXtreme gradient boosting (XGB), light gradient-boosting (LGB), cat boosting (CB), random forest (RF), k-nearest neighbors (kNN), and long short-term memory (LSTM) models. Sub-hourly SM, electrical conductivity (EC), soil temperature (ST), and weather parameters were collected during research experiments conducted for two years (2020–2021 and 2021–2022) at the Tropical Research and Education Center (TREC), University of Florida. A network of SM sensors and a weather station were installed at the experimental site with 24 plots of green beans and sweet corn under full and three deficit irrigation treatments with three replications. Model performance metrics such as coefficient of determination (r²) and global performance indicator (GPI) were used to evaluate the performance of the models. Results showed that all MLs and DL models performed more than satisfactorily in simulating SM of green beans and sweet corn plots. The testing average r² and GPI of MLs were 0.83 and 0.02 (green beans) and 0.85 and 0.02 (sweet corn). However, XGB and LGB models outperformed the remaining ML and DL models. The testing r² and GPI of XGB were 0.86 and 0.014 for green beans, whereas 0.88 and 0.015 for sweet corn. The r² and GPI values for the LGB were 0.85 and 0.014 for green beans, while 0.88 and 0.015 for sweet corn. Even though DL model took longer and resources to be trained, its performance was not as accurate as that of XGB and LGB models. However, the performance of DL was better than the LSTM model. The r² and RMSE of the LSTM model were 0.68 and 0.02cm ³ cm^-3 for green beans and 0.75 and 0.02cm ³ cm^-3 for sweet corn, respectively. Whereas the r² and RMSE of DL were 0.84 and 0.015cm ³ cm^-3 (green beans) and 0.85 and 0.02 cm ³ cm^-3 (sweet corn). The ML and DL models performed better in simulating SM of sweet corn plots than green beans. Overall, these results confirmed that the ML and DL models could be alternative tools for SM prediction for agricultural fields, with potential applications for irrigation scheduling and water resources management.

The chlorophyll content is a vital indicator of rice growth and nutritional status. However, estimating the rice chlorophyll content using spectral-based techniques at the early tillering stage is challenging because of background interference. Using the energy conservation principle, this study explained the spectral variation and background interference mechanisms of clear, muddy, and green algae-covered backgrounds. We developed mathematical interference models for the three types of backgrounds and determined their interference degree and influence mode. We developed rice chlorophyll content estimation models for unclassified and classified (clear, muddy, and green algae-covered) backgrounds using 12 preprocessing, four wavelength selection, and three modeling methods, and we explored the importance of background classification. Moreover, we found that the optimal chlorophyll content estimation model for the clear background was SS+UVE+CNN, with R² and RMSE values of 0.786 and 13.191 in the training set and 0.741 and 15.327 in the test set, respectively; that for the muddy background was MSC+GA+CNN, with R² and RMSE values of 0.914 and 10.425 in the training set and 0.660 and 16.844 in the test set, respectively; and that for the green algae-covered background was DC+GA+CNN, with R² and RMSE values of 0.904 and 9.111 in the training set and 0.688 and 17.694 in the test set, respectively. Our study could provide valuable insights into reducing and correcting background interference during proximal remote sensing data collection.

Body temperature is a critical indicator of the health and productivity of egg-laying chickens and other domesticated animals. Recent advancements in thermography allow for precise surface temperature measurement without physical contact with animals, reducing animal stress from human handling. Gold standard temperature analysis via thermography requires manual selection of limited points for an object of interest, which could be time-consuming and inadequate for representing the comprehensive thermal profile of a chicken’s body. The objective of this study was to leverage and optimize a zero-shot artificial intelligence technology for the automatic segmentation of individual cage-free laying hens within thermal images, providing insights into their overall thermal conditions. A zero-shot image segmentation model (Segment Anything, “SAM”) was modified by replacing manual selections of target points with automatic selection of the initial point using pre-processing techniques (e.g., thresholding) in each thermal image. The model was also incorporated with post-processing techniques integrated with a machine learning classifier to improve segmentation accuracy. Three versions of modified SAM models (i.e., SAM, FastSAM, and MobileSAM), two common instance segmentation algorithms (i.e., YOLOv8 and Mask R-CNN), and two foundation segmentation models (i.e., U²-Net and ISNet) were comparatively evaluated to determine the optimal one for bird segmentation from thermal images. A total of 1,917 thermal images were collected from cage-free laying hens (Hy-Line W-36) at 77–80 weeks of age. The image dataset exhibited considerable variations such as feathers, bird movement, body gestures, and the specific conditions of cage-free facilities. The experimental results demonstrate that the modified SAM did not only surpass the six zero-shot models—YOLOv8, Mask R-CNN, FastSAM, MobileSAM, U²Net, and ISNet—but also outperformed other modified SAM-based models (Modified FastSAM and Modified MobileSAM) in terms of hen detection performance, achieving a success rate of 84.4 %, and in segmentation performance, with an intersection over union of 85.5 %, recall of 91.0 %, and an F1 score of 92.3 %. The optimal model, modified SAM, was pipelined to extract statistics including the averages (°C) of mean (27.03, 27.04, 28.53, 26.68), median (26.27, 26.84, 28.28, 26.78), 25th percentile (25.33, 25.61, 27.26, 25.53), and 75th percentile (28.04, 27.95, 29.22, 27.55) of surface body temperature of individual laying hens in thermal images for each week. More statistics of hen body surface temperature can be extracted based on the segmentation results. The developed pipeline is a useful tool for automatically evaluating the thermal conditions of individual birds.

Efficient management and accurate monitoring are crucial for the sustainable development of the aquaculture industry. Traditionally, monitoring methods have relied on single-modality approaches (e.g., physical sensors, vision, and audio). However, these methods are limited by environmental interference and inability to comprehensively capture the complex characteristics of aquatic organisms, leading to data bias, low identification accuracy, and poor model portability across different settings. In contrast, multimodal fusion technologies have emerged as a promising solution for intelligent aquaculture due to their strong environmental adaptability, information complementarity, and high generalization ability. Despite this potential, there is a lack of comprehensive literature reviewing the transition from single-modal to multimodal systems in aquaculture. This paper addresses this gap by presenting a systematic review of both single-modal and multimodal fusion technologies in aquaculture over the past two decades. We analyze the strengths and limitations of each approach, focusing on four key areas: water quality monitoring, feeding behavior analysis, disease prediction, and biomass estimation. Through this comprehensive analysis, we provide theoretical and practical insights into the application of multimodal fusion technology in aquaculture, highlighting its potential to enhance efficiency and sustainability while overcoming current limitations.

The commonly used universal nutrient solution formula for facility-grown lettuce leads to excessive nitrogen fertilizer usage, low utilization efficiency, and severe environmental pollution. This formula keeps the nitrogen fertilizer concentration consistently high throughout the growth stages of lettuce, which is not conducive to lettuce growth because its nitrogen needs vary across different developmental stages. To address these inefficiencies, this study introduces a method for determining an appropriate interval limits for nitrogen concentration regulation for greenhouse lettuce cultivation based on chlorophyll fluorescence parameters. A single-factor experiment was designed to gather a dataset of chlorophyll fluorescence and biomass parameters at varying nitrogen concentrations and growth stages. Initial findings using the maximal information coefficient correlation analysis indicated that no single fluorescence parameter alone was sufficient for optimal regulation. Thus, the analytic hierarchy process was employed to dynamically determine the weights for comprehensive fluorescence parameters. The U-chord curvature method was then used to calculate the response curve’s upper and lower interval limits. The Technique for Order Preference by Similarity to an Ideal Solution method confirmed the rationality of the nitrogen concentration intervals for different stages, which achieved the highest comprehensive scores. Implementing these intervals led to a 49.7 % reduction in nitrogen fertilizer usage, with no significant difference in dry weight at the lower limit but a 36.2 % reduction with a 9.4 % increase in dry weight at the upper limit compared with the universal nutrient solution formula. This approach significantly reduces the use of ineffective nitrogen fertilizers while maintaining crop yield, offering a more environmentally friendly and efficient method for managing nitrogen for lettuce cultivation in greenhouses.

Smart agriculture (SA), which combines the Internet of Things (IoT) with a variety of smart devices including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and computing systems, is an emerging technology that shows how far the agricultural sector has progressed. Usage of edge computing devices on farms has been growing in the past decades for increasing the yields by improving resource use efficiency through the utilization of temporal, spatial, and individual farm data. With the growing adoption of digital technology, the agricultural sector now offers tools and services for retaining, storing, and analyzing the vast amounts of data produced by Smart Agricultural systems. However, this industry is more vulnerable to cyber security risks due to its growing reliance on technology. This article presents a comprehensive assessment of the state-of-the-art consequences of SA and current cyber security concerns. In addition, this article delves into the structural framework of SA, thoroughly addressing the major security threats at each layer. This study also provides a complete overview of major developments and future research directions in agricultural cyber security for SA. These valuable insights into cyber security will encourage cyber security researchers to suggest more creative and innovative ideas in the future.

Accurate and rapid estimation of cabbage head diameters is critical for precise decision-making in cabbage-harvesting equipment, thereby ensuring the quality of cabbage head harvesting. However, mature cabbage heads are enveloped by layers of outer leaves, resulting in varying degrees of occlusion, which poses significant challenges for direct detection and diameter measurement of cabbage heads. To address this problem, this study proposes a method based on the keypoint of cabbage head for estimating cabbage head diameters with different degrees of occlusion in the field. An improved deep learning model, YOLOv8n-Cabbage Keypoints (YOLOv8n-CK), is introduced to accurately and rapidly detect the keypoints of cabbage heads. Specifically, to enhance the attention of the network to occluded cabbage head features in complex images, the convolutional block attention module (CBAM) is introduced in the backbone, thereby improving the accuracy of the model in detecting the keypoints of occluded cabbage heads. Moreover, to balance the accuracy and speed of the keypoint detection network, all the Conv modules of the C2f-Bottleneck structure are replaced by Ghost modules, which effectively reduces the number of parameters in the model while maintaining its accuracy and reducing the computational complexity. Based on the results of keypoints detection, the physical diameter of cabbage heads is computed by integrating the depth information of the effective keypoints using a histogram filtering algorithm. The experimental results show that for varying degrees of occlusion, YOLOv8n-CK achieves an average precision (AP_50–95) of 99.2 % in detecting cabbage head keypoints, with 12.68 % and 13.04 % reductions in the params and floating point operations per second, respectively, compared to the original model. The mean absolute percentage error of the cabbage head diameter estimation model is 4.28 ± 0.13 %, and it exhibits favorable performance even under heavy occlusion (occlusion rate >65 %). Validation on an edge computing device shows that the model achieves 142.6 frames per second, which satisfies the real-time diameter estimation requirements for cabbage heads. These findings confirm the effective in-situ measurement of cabbage head diameters in the field, offering innovative insights for the development of efficient and low-damage harvesting equipment for cabbage.

In this study, in order to solve the problem of low seeding performance of the centrifugal high-speed precision seed metering device for maize during high-speed seeding, a method of airflow-assisted seed filling and cleaning to improve seeding performance is proposed. A trapezoidal opening is designed at the bottom of the hole insert and an airflow outlet is designed on the front shell to perform airflow-assisted seed filling and cleaning processes. By combining the discrete element method and computational fluid dynamics method for simulation, the optimal trapezoidal opening length is 14 mm, and the optimal number of holes for airflow outlet 2 is 11 holes. When the seeding speed was 12 km/h, 15 km/h, and 18 km/h, the optimal inlet air pressure was 800 Pa, 800 Pa, and 900 Pa respectively. The results of the bench experiment show that when the seeding speed was 12 km/h, the omission, repeated, and pass indexes were 2.75 %, 5.12 %, and 92.13 % respectively. When the seeding speed was 15 km/h, the omission, repeated, and pass indexes were 2.02 %, 4.17 %, and 93.81 % respectively. When the seeding speed was 18 km/h, the omission, repeated, and pass indexes were 3.16 %, 3.98 %, and 92.86 % respectively. The power of the air-suction seed metering device was 471.65 W when the seeding speed was 12 km/h. The centrifugal high-speed precision seed metering device for maize with airflow-assisted seed filling and cleaning has only a power of 45.84 W when seeding at a speed of 18 km/h. It not only has a faster seeding speed, but also consumes only 9.8 % of the energy compared to traditional pneumatic seed metering device, which can reduce environmental pollution.

In recent years, the use of convolutional neural networks (CNNs) and graph convolutional networks (GCNs) has significantly advanced hyperspectral image classification (HSIC). Despite these achievements, the challenge of limited labeled samples remains a critical obstacle when employing CNNs and GCNs for hyperspectral image classification. Agricultural images often face challenges due to high spectral variability and complex spatial patterns, making accurate classification difficult. Additionally, the presence of noise and limited labeled data further complicates the analysis and interpretation of these images. Although graph convolution networks and their predecessors have greatly advanced the use of spatial relationship features, deep learning algorithms, such as convolutional neural networks (CNNs), have reduced the need for and reliance on a high number of samples through spatial feature learning and consideration. Therefore, to advance the field, a novel approach termed “dimension reduction fuzzy graph network” (DRFG) was designed. This approach is a combination of deep fuzzy-based DR, enhanced with 3D-CNN and GATs, with the application of principal component analysis (PCA) for optimized DR. The DRFG model entails two major processing stages. The initial stage involves the classification of the raw data cube using the 3D-CNN. In the second stage, the results are processed by means of an algorithm enriched by lightweight GAT-based modules. The DRFG model combines morphological features selection from fuzzy C-means (FCM) clustering and optimized DR by using PCA. Thus, the model employs the best of PCA and GATs in order to allow for optimized classification. At high-performance optimal DR, the DRFG model offers optimal multispectral imaging as well as the analysis and classification of hyperspectral data, which is sufficiently promising so as to advance the field’s needs for precision agriculture.

Fourier transform mid-infrared spectroscopy (FT-MIRS) technique has been extensively employed for performance measurement of dairy cows and dairy herd improvement (DHI), but different milk analyzers have shown significant differences in the sensitivity, laser intensity, and stability of FT-MIRS determination, which cannot be directly integrated and applied in phenotype prediction and relevant studies. Existing literature has reported several FT-MIRS calibration methods such as piecewise direct standardization (PDS) and retroactive percentile standardization (RPS), achieving good standardization results. However, these methods require to be optimized because they take no account of the collinearity and redundancy of the spectrum.

Therefore, this study established an improved agglomerative clustering piecewise direct standardization (ACPDS) method. This study used 432 standard milk samples prepared by the standard laboratory within 4 months (based on the standard sample preparation procedures in the International Dairy Federation Guidelines for the Application of Mid-infrared Spectroscopy) and carried out FT-MIRS measurements and data collection on 9 instruments in 5 DHI laboratories. Meanwhile, the new method established in this study together with the existing methods of single wavelength standardization (SWS) and PDS were adopted to standardize the spectra collected on 9 instruments. The reproducibility, computation time, memory usage, and repeatability of the milk component prediction models were verified and compared.

The results revealed that ACPDS exhibited significant advantages over SWS and PDS, with a higher level of spectral reproducibility, and there was a significant advantage in the repeatability of the milk component prediction models but no significant increase in memory usage. The impact of its application across regions, months, and years was insignificant. In addition, based on the respective characteristics of ACPDS and the existing two methods, application strategies have been proposed for these three methods, providing new technologies and laying the foundation for the FT-MIRS-based milk component prediction models, widespread performance measurement of dairy cows in different instruments and at different times, and comparative analysis on the traits and phenotypes of dairy cows as well as their joint breeding in China and even the world.