Precision Agriculture最新文献

There are currently many in-field methods for estimating soil properties (e.g., pH, texture, total C, total N) available in precision agriculture, but each have their own level of suitability and only a few can be used for direct determination of plant-available nutrients. As promising approaches for reliable in-field use, this review provides an overview of electromagnetic, conductivity-based, and electrochemical techniques for estimating plant-available soil nutrients and pH. Soil spectroscopy, conductivity, and ion-specific electrodes have received the most attention in proximal soil sensing as basic tools for precision agriculture during the last two decades. Spectral soil sensors provide indication of plant-available nutrients and pH, and electrochemical sensors provide highly accurate nitrate and pH measurements. This is currently the best way to accurately measure plant-available phosphorus and potassium, followed by spectral analysis. For economic and practicability reasons, the combination of multi-sensor in-field methods and soil data fusion has proven highly successful for assessing the status of plant-available nutrients in soil for precision agriculture. Simultaneous operation of sensors can cause problems for example because of mutual influences of different signals (electrical or mechanical). Data management systems provide relatively fast availability of information for evaluation of soil properties and their distribution in the field. For rapid and broad adoption of in-field soil analyses in farming practice, in addition to accuracy of fertilizer recommendations, certification as an official soil analysis method is indispensable. This would strongly increase acceptance of this innovative technology by farmers.

Background

Remote sensing based on multispectral imaging may be useful for detecting vegetation stress responses in agriculture.

Objectives

To evaluate the potential of orbital multispectral imaging in discriminating the most effective strategies for reducing plant-parasitic nematode populations, thereby preventing yield losses in coffee production.

Methods

Coffee plants were treated with eleven treatments, including Bacillus spp. isolates, commercial biological products, commercial chemical nematicides, and water (control group). Initial and final nematode populations in the soil were quantified, and surface reflectance data were collected using the Planet orbital multispectral sensor. The data were classified using the random tree algorithm.

Results

The population of plant-parasitic nematodes was reduced by 35.90% and 55.13% following the application of B. amyloliquefaciens isolate B266 and B. subtilis isolate B33, respectively. Under the conditions of this experiment, multispectral imaging accurately discriminated the most nematicidal treatments, with a global accuracy of 80%.

Conclusions

Orbital multispectral imaging can discriminate the most effective treatments used for nematode management in coffee plants, highlighting its potential as a supportive tool in agriculture.

Introduction

Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness, DDMs can be complemented by inputs derived from mechanistic models (MMs).

Methods

This study investigated enhancing the predictive quality of DDMs by using as features a combination of MMs outputs, specifically biomass and soil moisture, with conventional data sources like satellite imagery, weather, and soil information. Four experiments were performed with different datasets being used for prediction: Experiment 1 combined MM outputs with conventional data; Experiment 2 excluded MM outputs; Experiment 3 was the same as Experiment 1 but all conventional temporal data were omitted; Experiment 4 utilised solely MM outputs. The research encompassed ten field-years of wheat and chickpea yield data, applying the eXtreme Gradient Boosting (XGBOOST) algorithm for model fitting. Performance was evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC).

Results and conclusions

The validation results showed that the XGBOOST model had similar predictive power for both crops in Experiments 1, 2, and 3. For chickpeas, the CCC ranged from 0.89 to 0.91 and the RMSE from 0.23 to 0.25 t ha⁻¹. For wheat, the CCC ranged from 0.87 to 0.92 and the RMSE from 0.29 to 0.35 t ha⁻¹. However, Experiment 4 significantly reduced the model's accuracy, with CCCs dropping to 0.47 for chickpeas and 0.36 for wheat, and RMSEs increasing to 0.46 and 0.65 t ha⁻¹, respectively. Ultimately, Experiments 1, 2, and 3 demonstrated comparable effectiveness, but Experiment 3 is recommended for achieving similar predictive quality with a simpler, more interpretable model using biomass and soil moisture alongside non-temporal conventional features.

Purpose

This paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location.

Methods

The method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution.

Results

The tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88.

Conclusion

The proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.

Purpose

The adoption of the Internet of Things (IoT) technology in the agricultural sector has enormous potential for improving productivity, efficiency, and sustainability. Understanding the predictors affecting the acceptance of IoT-enabled agricultural systems (IAS) is crucial for policymakers, researchers, and industry practitioners.

Methods

This study adopted a cross-sectional design, collected quantitative data from 458 agro-entrepreneurs through structured interviews during July 2022, and applied partial least squares structural equation modeling for data analysis.

Results

The findings revealed that perceived need for IAS (β=0.187) and tolerance of diversity (β=0.166) positively linked with the attitude towards IAS, whereas attitude towards IAS (β=0.262), knowledge about IAS (β=0.309), industry influence (β=0.223), and IoT compatibility (β=0.274) have a positive effect on agroentrepreneurs’ intentions to adopt IAS at the 1% level of significance. Finally, the intention to adopt IAS shows a positive effect (β=0.442) on the adoption of IAS among the Chinese agro-entrepreneurs at the 1% level of significance. Using a multigroup analysis, this study also examined the associations based on the respondents’ age, gender, education level, land size, and monthly income.

Conclusion

This study establishes its originality by examining the relationship between original constructs derived from the theory of planned behavior and contextual factors, such as perceived need, industry influence, tolerance of diversity, innovativeness, knowledge, and compatibility, and investigating the relevant factors, thereby enhancing the comprehension of technology adoption processes in the agricultural sector. The results provide guidance to policymakers and professionals in formulating approaches to encourage the use of IoT in agriculture, supporting the objectives of the "Agriculture 4.0 Policy" and "Digital Rural Development Strategy" in China, and promoting sustainable development goals (SDG 13).

Purpose

High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes.

Methods

A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (g_s), leaf (Ψ_leaf) and stem (Ψ_stem) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements.

Results

Results revealed the importance of TIR variables to estimate physiological proxies (g_s, Ψ_leaf, Ψ_stem) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period.

Conclusion

This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration.

Purpose

Site-specific field management operations represent one of the fundamental principles of precision viticulture. The purpose of the research is to observe and analyse the evolution of a vineyard over three consecutive years to understand which factors most significantly influence the quality of the vineyard’s production.

Methods

The research involved technologically advanced tools for crop monitoring, such as remote and proximal sensors for vegetation surveys. In association, grape quality analyses were performed through laboratory analysis, constructing geostatistical interpolation maps and matrix correlation tables.

Results

Both remote and proximal sensing instruments demonstrated their ability to effectively estimate the spatial distribution of vegetative and quality characteristics within the vineyard. Information obtained from GNDVI and CHM proved to be valuable and high-performance tools for assessing field variability. The differentiated plant management resulted in uniform production quality characteristics, a change evident through the monitoring techniques.

Conclusion

The research highlights the effectiveness of using advanced technological instruments for crop monitoring and their importance in achieving uniformity in production quality characteristics through differentiated plant management. From the results obtained, it was possible to observe how differentiated plant management led to a uniformity of production quality characteristics and how the monitoring techniques can observe their evolution. This result represents a positive accomplishment in field management during the three monitoring years, responding to the principles and objectives of precision agriculture.

Context

Process-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).

Objective

We calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.

Methods

We conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.

Results and conclusions

The APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.

Yield forecasting and within-field yield variation is essential information that helps farmers develop sustainable agriculture. However, such information still needs to be included for most of them, and remote sensing is an alternative to provide it. Our objective was to assess Random Forest regression models composed of unique GLCM texture measures as an alternative to usual empirical models that use spectral response and auxiliary data, which is complex and reaches varied results. Eleven GLCM texture models based on eight texture measures of a single spectral layer were assessed to represent soybean field yield variation in two sites and seasons. Several models achieved satisfactory results, reaching R² from 0.90 to 0.95 and RMSE from 0.06 to 0.26 t/ha. Models above 15-window size are recommended for the soybean yield prediction as window size is an essential attribute to GLCM performance. Models derived from the bands individually (red, red-edge, near-infrared, and short wavelength infrared) were more sensitive to the window size than those derived from vegetation indices (EVI, GNDVI, GRNDVI, NDMI, NDRE, NDVI, SFDVI). The data aggregated by texture measures improve the individual spectral responses, providing alternatives to predict soybean within-field yield variation using random forest models.

Introduction

Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.

Materials and methods

This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.

Results

Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.

Conclusion

Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.