Precision Agriculture最新文献

The Małopolskie and Podkarpackie provinces in Poland are characterized by many small farms with many small, scattered fields. This farm structure is labeled “agrarian fragmentation”. Using digital technologies in such small farm areas is usually a challenge. However, there are several digital technologies that, with minimal financial investment, can yield results in the form of improved resource management and agricultural production processes, as well as data-driven decision-making. The overall objective of this analysis is to determine the limitations of using digital technologies in farms operating in areas with high agrarian fragmentation. In addition, the aim was also to identify the differences in the potential for implementing individual digital solutions depending on farm size and activity type conducted in the surveyed area. A survey was conducted by the Paper and Pen Personal Interview (PAPI) method, in which 389 farmers took part. Research showed that the technologies most commonly used in the study area include applications recognizing plant diseases and applications supporting decision-making. The use of advanced digital tools related to precision agriculture and the automation of crop production was very rare. Farm size, the age of the farmer managing the farm, and the number of farm activities were significant factors that increased the probability of implementing digital technologies. The main barriers to their implementation were a lack of sufficient knowledge and trust. The implementation of digital technologies in small farms requires actions aimed at increasing farmer knowledge. Meanwhile, designing new digital solutions must take the specific regional conditions into account, such as geographical factors or the limited investment capacity of farms.

Efficient water management is critical for sustainable agriculture in Mediterranean climates, particularly in super-high-density (SHD) olive orchards where water scarcity poses significant challenges. This study assessed the potential of UAV-based thermal and multispectral imagery to monitor crop water status and predict yield under different regulated deficit irrigation (RDI) strategies. Conducted over two seasons (2018–2019) in a commercial SHD olive orchard (Olea europaea L., cv. ‘Arbequina’) in Villena, Spain, the experiment involved four irrigation treatments ranging from full irrigation (FI) to progressively restricted RDIs. UAV flights captured thermal infrared and multispectral imagery at key phenological stages, to calculate Crop Water Stress Index (CWSI) and Normalized Difference Vegetation Index (NDVI), which were validated against plant-based measurements of stem water potential (Ψ_stem). The results demonstrated that thermal parameters, including canopy temperature and CWSI, effectively identified water stress levels, although their sensitivity was influenced by environmental conditions and sensor limitations. NDVI proved to be a reliable indicator of vegetative growth and yield, with values closely linked to irrigation levels and fruit load. The approach incorporating both canopy and soil reflectance (NDVI_crop+ground) provided the most accurate assessment of crop performance. These findings highlight the value of UAV-based remote sensing technologies for optimizing irrigation management in SHD olive orchards, particularly under deficit irrigation regimes. However, further advancements in sensor accuracy and index normalization are recommended to enhance their applicability and precision in agricultural practices.

The deep integration of artificial intelligence technology and agriculture has significantly propelled the rapid development of smart agriculture. However, the field still faces numerous challenges, including high algorithm complexity and limited detection speed in farmland environments. To address the challenges encountered by corn spraying robots in navigating and identifying lines, we have proposed a corn crop row navigation line recognition algorithm based on the LT-YOLOv10s model. By introducing lightweight network models (GhosNet), efficient feature pyramid models (SPPFA), and efficient feature attention modules (PSCA) into the YOLOv10s network, we have reduced the complexity of the model and significantly enhanced the detection efficiency of corn plants. Then, the algorithm precisely locates corn plants using the center points of detection boxes and accurately fits crop rows using the least squares method. Finally, the navigation lines centered on the corn crop rows are determined through the adjacent centerline method. Experimental data significantly demonstrates that the comprehensive performance of the LT-YOLOv10s model surpasses industry benchmark models such as YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and the traditional YOLOv10s. The proposed algorithm for extracting the center navigation line of corn crop rows boasts an average fitting time of just 26ms with an accuracy rate of up to 93.8%, ensuring precision and reliability in navigation line extraction. This provides robust technical support for precise navigation of corn-spraying robots.

Purpose

In contemporary agriculture, achieving sustainable food production while preserving the environment is crucial. Traditional uniform rate nitrogen fertilization (URNF) often leads to over- or under-applications of N in fields with negative economic, agronomic and environmental issues. Variable rate nitrogen fertilization (VRNF) has shown promise in optimizing N application by accounting for soil and crop variability, thus improving nitrogen use efficiency and reducing environmental impact. This study evaluates and compares two VRNF solutions in two wheat fields in Belgium and France.

Methods

The first, VRNF1 relied on onsite measurement of soil nitrate using ion-selective electrode (ISE) sensors, whereas the second, VRNF2, utilizes the fusion of on-line measured key soil properties using a visible and near-infrared spectrometer (vis-NIRS) and crop normalized difference vegetation index (NDVI). In VRNF1, soil nitrate values were used to rank the fertility level of management zones (MZs), delineated by the clustering analysis of vis-NIRS-NDVI data (like for VRNF2), with N fertilization rates adjusted by 30–50%, applying lower rates to high-fertility zones and higher rates to low-fertility zones. In VRNF2, after the fertility level of MZ was ranked by examining the on-line measurements of pH, organic carbon (OC), moisture content (MC), potassium (K), phosphorus (P), and calcium (Ca), and crop NDVI, N fertilizer rates were adjusted similarly to VRNF1.

Results

A cost-benefit analysis revealed that the gross margin of both VRNF solutions was larger than that of the URNF, with VRNF1 providing up to 289 EUR ha⁻¹ and VRNF2 up to 358 EUR ha⁻¹ more gross margin than URNF. VRNF1 increased crop yield by up to 8%, while VRNF2 resulted in a 9.2% yield increase compared to URNF. However, VRNF1 achieved a slight economic advantage (14 EUR ha⁻¹) in one field, while VRNF2 was more profitable in the other field by 69 EUR ha⁻¹. Additionally, VRNF2 demonstrated superior environmental benefits, using 14% less fertilizer than URNF and 12% less than VRNF1.

Conclusion

Overall, VRNF2 offered better economic and environmental outcomes than VRNF1 and URNF. However, the subjectivity of ranking MZs into different fertility levels in the absence of historical yield data for the VRNF2 raises concerns, calling in such a situation for VRNF1 to be adopted in future VRNF schemes.

Assessing spatial and temporal variations in crop water stress is vital for precision irrigation. This study utilized Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MSS) and thermal band (TB) sensors to map the crop water stress index (CWSI) in wheat. A water deficit experiment was conducted on winter wheat under varying irrigation levels during late vegetative, reproductive, and maturation stages. CWSI was calculated using canopy temperature, ambient air temperature, and vapor pressure deficit (VPD). Six machine learning (ML) models—linear model (LM), random forest (RF), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGB), and artificial neural network (ANN)—were developed for pre-heading, post-heading, and seasonal datasets. The top five vegetation indices (VIs), selected using Recursive Feature Elimination (RFE), along with thermal data, were used as inputs to the ML models. Results showed that seasonal ML models outperformed those based only on pre-heading or post-heading data. Particularly, the RF model performed well, with respective R² and RMSE values of 0.87 and 0.09 for seasonal, 0.82 and 0.05 for pre-heading, and 0.93 and 0.06 for post-heading datasets. SHapley Additive exPlanations (SHAP) analysis identified Red Normalized Value (RNV), TB, and Green Red Vegetation Index (GRVI) as key predictors of CWSI in the RF model. CWSI maps effectively captured spatial variations in water stress, aligning with irrigation management practices. This study demonstrates the effectiveness of combining UAV remote sensing and ML for precision irrigation management.

Purpose

The hull scrape and vegetation indices are widely used for predicting peanut maturation, but they are time-consuming, subjective, labor-intensive, and fail to account for climate variables, reducing their accuracy.Thus, the objective was to verify the potential of using artificial intelligence associating IV and climate variables to predict the variability of peanut pod maturity in the field

Methods

For this purpose, peanut maturity data collected on different dates in commercial fields in Brazil and the United States. In addition, high-resolution satellite images were used to calculate nine IV and four climatic variables for each area were acquired using the NASA-POWER platform. Four machine learning models were tested and the input for the training were selected using the Random Forest feature selection. Thus, the models were trained using 70% of the data for training and 30% for testing and applied the cross validation with K-fold.

Results

The best results were obtained for the XGBoosting model with R² test values varying 0.90, 0.89, 0.93 and 0.87 and a minimum MAE and RMSE of 0.05. Except for the Georgia dataset where the MLP model presents the highest performance R² value of 0.93, MAE 0.05 and RMSE 0.06 for the test. The RBF models present the worst results with a low index of agreement (d) 0.4 for all the datasets demonstrating a low agreement between the predicted and observed values.

Conclusion

Combining the climatic variables was able to improve the model’s performance, however detailed information about the field such as topographic conditions and soil type seem to be a different approach to enhance the model performance. Using the calibrated model for overall dataset peanut farmers from any localities can use to monitor and map the PMI variability in the fields, improve the decision-making, decrease the loss and increase the kernels quality.

Purpose

Precision agriculture technologies play an important role in optimising practices to increase yields and reduce costs, contributing to socio-economic progress and environmental well-being, and playing a key role in addressing climate change. Viticulture is a strategic, input-intensive agricultural sector where precision technologies can make the use of resources more efficient without compromising profitability. The aim of this study is to evaluate the profitability of implementing precision farming systems, such as unmanned aerial vehicle surveying for the production of vigour maps, compared to the conventional cultivation system in two Italian wineries.

Methods

The profitability of using precision farming tools in viticulture compared to conventional management techniques has been investigated in two Italian wineries over a four-year period, before and after the introduction of UAV technology.

Results

The results demonstrate the usefulness and economic viability of precision agriculture technologies in viticulture. The vigour maps produced by the data collected with UAV technology allow both the identification of problems such as diseases, and consequently the planning of phytosanitary treatments, and selective grape harvesting, which allows a significant improvement in the quality of the harvested grapes.

Conclusion

The results demonstrate the usefulness of precision technologies for cost-effective and sustainable vineyard management, satisfying a market segment made up of stakeholders who are increasingly sensitive to environmental issues.

Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made to assess nitrogen content (NC) at the organ level and its association with nitrogen use efficiency (NUE). Also, little is known about the performance of UAV-based image texture features of different spectral bands in monitoring crop nitrogen and NUE. In this study, multi-spectral images were collected throughout different stages of winter wheat in two independent field trials - a single-variety field trial and a multi-variety trial in 2021 and 2022, respectively in China and Germany. Forty-three multispectral vegetation indices (VIs) and forty texture features (TFs) were calculated from images and fed into the partial least squares regression (PLSR) and random forest (RF) regression models for predicting nitrogen-related indicators. Our main objectives were to (1) assess the potential of UAV-based multispectral imagery for predicting NC in different organs of winter wheat, (2) explore the transferability of different image features (VI and TF) and trained machine learning models in predicting NC, and (3) propose a technical workflow for mapping NUE using UAV imagery. The results showed that the correlation between different features (VIs and TFs) and NC in different organs varied between the pre-anthesis and post-anthesis stages. PLSR latent variables extracted from those VIs and TFs could be a great predictor for nitrogen agronomic efficiency (NAE). While adding TFs to VI-based models enhanced the model performance in predicting NC, inconsistency arose when applying the TF-based models trained based on one dataset to the other independent dataset that involved different varieties, UAVs, and cameras. Unsurprisingly, models trained with the multi-variety dataset show better transferability than the models trained with the single-variety dataset. This study not only demonstrates the promise of applying UAV-based imaging to estimate NC in different organs and map NUE in winter wheat but also highlights the importance of conducting model evaluations based on independent datasets.

Through targeted spray applications, precision agriculture can provide not only environmental benefits but also lower production costs, improving farm competitiveness. Nevertheless, few studies have focused on the cost-effectiveness of precision agriculture sprayers in vineyards, which are among the most widespread specialty crops. Therefore, this is the first study that aims to evaluate the cost-effectiveness of variable rate technology (VRT) and unmanned aerial vehicle (UAV) sprayers compared to a conventional sprayer in a hypothetical and representative vineyard area of southern Italy. The economic analysis, based on technological parameters in the literature, enabled the identification of the minimum farm size (break-even point) for introducing precision agriculture sprayers (PAS), considering the annual cost of the pesticide treatments (equipment and pesticide costs). Our findings revealed that the UAV sprayer—if permitted by law—could be the most convenient option for farms larger than 2.27 ha, whereas the VRT sprayer should be chosen by farms over 17.02 ha. However, public subsidies, such as those provided by the Italian Recovery Plan, make adopting VRT sprayers also economically viable for areas as small as 3.03 ha. Finally, the sensitivity analysis confirmed that the purchase price and pesticide cost are the most sensitive parameters affecting the break-even points. Our findings shed light on the economic sustainability of these innovative sprayers, a key driver for their adoption by farmers and for setting future strategies for facing the current agricultural crisis.