Published: Dec. 20, 2024
Language: Английский
Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 1975 - 1975
Published: Sept. 1, 2024
Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way realize the accurate management decision support of production processes using modern information technology, is becoming an effective method solving these In particular, combination remote sensing technology machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive systematic reviews on integrated application two technologies. For this reason, study conducts literature search Web Science, Scopus, Google Scholar, PubMed databases analyzes in PA over last 10 years. The found that: (1) because their varied characteristics, different types data exhibit significant differences meeting needs PA, which hyperspectral most widely used method, accounting more than 30% results. UAV offers greatest potential, about 24% data, showing upward trend. (2) Machine displays obvious advantages promoting development vector algorithm 20%, followed by random forest algorithm, 18% methods used. addition, also discusses main challenges faced currently, such difficult problems regarding acquisition processing high-quality model interpretation, generalization ability, considers future trends, intelligence automation, strengthening international cooperation sharing, sustainable transformation achievements. summary, can provide ideas references combined with promote
Language: Английский
Citations
9
Journal of Fungi, Journal Year: 2025, Volume and Issue: 11(1), P. 77 - 77
Published: Jan. 18, 2025
This review delves into innovative technologies to improve the control of vascular fungal plant pathogens. It also briefly summarizes traditional biocontrol approaches manage them, addressing their limitations and emphasizing need develop more sustainable precise solutions. Powerful tools such as next-generation sequencing, meta-omics, microbiome engineering allow for targeted manipulation microbial communities enhance pathogen suppression. Microbiome-based include design synthetic consortia transplant entire or customized soil/plant microbiomes, potentially offering resilient adaptable strategies. Nanotechnology has advanced significantly, providing methods delivery biological agents (BCAs) compounds derived from them through different nanoparticles (NPs), including bacteriogenic, mycogenic, phytogenic, phycogenic, debris-derived ones acting carriers. The use biodegradable polymeric non-polymeric eco-friendly NPs, which enable controlled release antifungal while minimizing environmental impact, is explored. Furthermore, artificial intelligence machine learning can revolutionize crop protection early disease detection, prediction outbreaks, precision in BCA treatments. Other genome editing, RNA interference (RNAi), functional peptides efficacy against pathogenic fungi. Altogether, these provide a comprehensive framework management diseases, redefining modern agriculture.
Language: Английский
Citations
1
Plants, Journal Year: 2024, Volume and Issue: 13(11), P. 1498 - 1498
Published: May 29, 2024
Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing growth efficiency yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years field experiments (2021–2022), soybean (Glycine max L.) corresponding UAV images were collected. Vegetation indices, canopy texture features, randomly extracted indices in combination, which exhibited strong correlations previous studies parameters, established. By analyzing the correlation between these parameters moisture, significantly correlated coefficients (p < 0.05) selected as input variables model (combination 1: vegetation indices; combination 2: features; 3: combination; 4: indices). Subsequently, extreme learning machine (ELM), gradient boosting (XGBoost), back propagation neural network (BPNN) utilized content. The results indicated that most higher compared while could enhance some extent. RDTI, random index, showed highest coefficient at 0.683, being Variance1 Correlation5. When 4 indices) XGBoost employed monitoring, level achieved study. determination (R2) estimation validation set reached 0.816, a root-mean-square error (RMSE) 1.404 mean relative (MRE) 1.934%. study foundation monitoring offering valuable insights rapid assessment growth.
Language: Английский
Citations
4
Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 224, P. 109232 - 109232
Published: July 15, 2024
Language: Английский
Citations
4
Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 1920 - 1920
Published: Aug. 27, 2024
The frequent occurrence of global climate change and natural disasters highlights the importance precision agricultural monitoring, yield forecasting, early warning systems. data assimilation method provides a new possibility to solve problems low accuracy prediction, strong dependence on field, poor adaptability model in traditional applications. Therefore, this study makes systematic literature retrieval based Web Science, Scopus, Google Scholar, PubMed databases, introduces detail strategies many remote sensing sources, such as satellite constellation, UAV, ground observation stations, mobile platforms, compares analyzes progress models compulsion method, parameter state update Bayesian paradigm method. results show that: (1) platform shows significant advantages agriculture, especially emerging constellation UAV assimilation. (2) SWAP is most widely used simulating crop growth, while Aquacrop, WOFOST, APSIM have great potential for application. (3) Sequential strategy algorithm field assimilation, ensemble Kalman filter algorithm, hierarchical considered be promising (4) Leaf area index (LAI) preferred variable, soil moisture (SM) vegetation (VIs) has also been strengthened. In addition, quality, resolution, applicability sources are key bottlenecks that affect application development agriculture. future, tends more refined, diversified, integrated. To sum up, can provide comprehensive reference by using model.
Language: Английский
Citations
4
Microchemical Journal, Journal Year: 2024, Volume and Issue: 206, P. 111542 - 111542
Published: Sept. 1, 2024
Language: Английский
Citations
4
CABI Reviews, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 26, 2025
Abstract Spectral imaging is a technique that captures and analyzes the spectral information of an object, such as its reflectance, transmittance, or fluorescence. It has been widely used in various fields, remote sensing, food quality assessment. In recent years, also emerged promising tool for crop disease diagnosis, it can provide rapid, non-destructive, accurate detection plant pathogens symptoms. This review aims to concise overview principles, methods, applications, challenges diagnosis. First, we introduce basic sensing concepts types imaging, hyperspectral, multispectral imaging. Second, discuss main steps techniques involved analysis, image acquisition, processing, feature extraction, classification. Third, present some representative examples applications fungal, bacterial, viral, nematode infections. Finally, highlight importance artificial intelligence integration alongside current limitations future directions
Language: Английский
Citations
0
Plant Methods, Journal Year: 2024, Volume and Issue: 20(1)
Published: Dec. 19, 2024
Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of in the (Brassica rapa L. ssp. pekinensis) can provide agronomic benefits. Here, we propose a model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based YOLOv8, with addition dynamic snake convolution (DSConv) module improved loss function maximum possible distance intersection-over-union (MPDIoU), enhanced complex structures global characteristics under different growth conditions. To reduce difficulty acquiring diseased data, cycle-consistent generative adversarial network (CycleGAN) was used to simulate generate for multiple fields. The lightly infected plants achieved precision, recall, mean average precision (mAP), F1-score 81.3, 86.6, 87.7, 83.9%, respectively. DSConv-GAN outperforms other models terms speed, robustness, generalization. model combined software improve practicability proposed method. Our results demonstrate be effective intelligent farming tool that provides early, rapid, growing environments.
Language: Английский
Citations
2
Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15
Published: Aug. 27, 2024
Background Cotton pests have a major impact on cotton quality and yield during production cultivation. With the rapid development of agricultural intelligence, accurate classification is key factor in realizing precise application medicines by utilize unmanned aerial vehicles (UAVs), large devices other equipment. Methods In this study, insect pest model based improved Swin Transformer proposed. The introduces residual module, skip connection, into to improve problem that features are easily confused complex backgrounds leading poor accuracy, enhance recognition pests. 2705 leaf images (including three pests, aphids, mirids mites) were collected field, after image preprocessing data augmentation operations, training was performed. Results test results proved accuracy compared original increased from 94.6% 97.4%, prediction time for single 0.00434s. with seven kinds models (VGG11, VGG11-bn, Resnet18, MobilenetV2, VIT, small, base), respectively 0.5%, 4.7%, 2.2%, 2.5%, 6.3%, 7.9%, 8.0%. Discussion Therefore, study demonstrates significantly improves efficiency detection models, can be deployed edge such as thus providing an important technological support theoretical basis control precision drug application.
Language: Английский
Citations
1
Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4637 - 4637
Published: Dec. 11, 2024
Verticillium wilt (VW) represents the most formidable challenge in cotton cultivation, critically impairing both fiber yield and quality. Conventional resistance assessment techniques, which are largely reliant on subjective manual evaluation, fail to meet demands for precision scalability required advanced genetic research. This study introduces a robust evaluation framework utilizing feature selection optimization algorithms enhance accuracy efficiency of severity VW. We conducted comprehensive time-series UAV hyperspectral imaging (400 995 nm) canopy field environment different days after sowing (DAS). After preprocessing data extract wavelet coefficients vegetation indices, various methods were implemented select sensitive spectral features By leveraging these selected features, we developed machine learning models assess VW at scale. Model validation revealed that performance responded dynamically as progressed achieved highest R2 0.5807 DAS 80, with an RMSE 6.0887. Optimization made marked improvement SVM using all observation data, increasing from 0.6986 0.9007. demonstrates potential based enhancing management, promising advancements high-throughput automated disease assessment, supporting sustainable agricultural practices.
Language: Английский
Citations
1