Search Results (117)

Potatoes are an economically important crop in Virginia, USA, where growers must balance planting dates, nitrogen (N) management, and variable crop prices. Early planting exposes crops to low temperatures that limit growth, whereas late planting increases pest pressure and nutrient inefficiency. This study evaluated the effects of planting dates, N rates, and application timing on potato growth, yield, and pest incidence. We also assessed whether soil physicochemical properties could predict the presence of wireworms and plant-parasitic nematodes (PPNs) using complementary on-farm samples collected across Eastern Virginia between March and July 2023. Three planting dates (early-March, late-March, and early-April) were combined with five N rates (0, 146, 180, 213, and 247 kg N·ha⁻¹) under early- and late-application regimes. We collected data on plant emergence, flowering time, soil nitrate, biomass, tuber yield, pest damage, and UAS-derived metrics. Results showed that late-March planting with 180 kg N·ha⁻¹ achieved the highest gross profit while maintaining competitive yields (25.06 Mg·ha⁻¹), representing 24% and 6% improvements over traditional practices, respectively. Early-April planting produced the largest tubers, with a mean tuber weight 19% higher than the other planting dates. The Normalized Difference Red Edge Index (NDRE) was strongly correlated with N content in plant tissue (R² = 0.81; r ≈ 0.90), and UAS-derived plant area accurately predicted tuber yield 4–6 weeks before harvest (R² = 0.75). Wireworm damage was significantly higher in early-March plantings due to delayed insecticide application, while soil nitrate concentration and percent H saturation were identified as key predictors of wireworm presence. Although less effectively modeled due to limited sample size, PPN occurrence was influenced by potassium saturation and soil pH. Aligning planting dates and nitrogen applications with crop phenology, using growing degree days (GDD), enhanced nitrogen management, and yield prediction. Full article

Globally, potatoes are one of the major crops that significantly contribute to food security; hence, the field of machine learning has opened the gate for many advances in plant disease detection. For real-time agricultural applications, it has been found that real-time data processing is challenging; this is due to the limitations and constraints imposed by hardware platforms. However, such challenges can be handled by deploying simple and optimized AI models serving the need of accurate data classification while taking into consideration hardware resource limitations. Hence, the purpose of this study is to implement a customized and optimized convolutional neural network model for deployment on hardware platforms to classify both potato early blight and potato late blight diseases. Lastly, a thorough comparison between both embedded and PC simulation implementations was conducted for the three models: the implemented CNN model, VGG16, and ResNet50. Raspberry Pi3 was chosen for the embedded implementation in the intermediate stage and NVIDIA Jetson Nano was chosen for the final stage. The suggested model significantly outperformed both the VGG16 and ResNet50 CNNs, as evidenced by the inference time, number of FLOPs, and CPU data usage, with an accuracy of 95% on predicting unseen data. Full article

Phosphate tailings (PTs) are typical industrial byproducts that can rapidly neutralize soil acidity. However, their acid-neutralizing efficacy, long-term application optimization mechanisms, and high-yield regulation pathways for crops remain unclear. This study conducted a corn-potato crop rotation field trial on acidic soils, investigating the effect of different PT application rates (T: CK, 0 t·ha⁻¹; PTs-1, 6 t·ha⁻¹; PTs-2, 9 t·ha⁻¹; PTs-3, 15 t·ha⁻¹) in a multiple cropping system (C: late autumn potatoes (LAP)-early spring potatoes (ESP)-summer maize (SM)). The results showed that two consecutive applications of 9 t·ha⁻¹ of PTs produced optimal results, increasing the LAP yield by 12.82% and the soil quality by 76.51%, while improving the ESP soil quality by 46.21%. The higher yield was mainly attributed to a significant increase in the soil pH (0.72–1.58 units) and enhanced chemical and biological properties (higher exchangeable calcium (ExCa), exchangeable magnesium (ExMg), the total exchangeable salt base ion (TEB), and catalase (CAT) and urease (UE) content and lower soil exchangeable acidity (EA), exchangeable hydrogen ion (ExH), and exchangeable aluminum (ExAl) levels). Notably, a synchronized increase in the total phosphorus (TP) and total potassium (TK) during LAP cultivation, combined with simultaneous growth of TP, available nitrogen (AN), and available phosphorus (AP) during ESP cultivation, and a significant increase in TP and AP during SM cultivation, effectively promoted crop yield. Furthermore, continuous PT application significantly enriched phosphorus (P)-soluble functional bacteria, such as Actinomycetes and Chloroflexota, and enhanced the stability of bacterial-fungal cross-boundary networks. In summary, optimal acidity levels and favorable soil texture improved soil quality, consequently increasing corn and potato yields. This study reveals for the first time that PTs can substantially increase crop production via a synergistic mechanism involving acid-base balance, structural improvement, and microbial activation. Not only does this provide a novel strategy for rapidly improving acidic soils, but it also establishes a solid theoretical and technical foundation for utilizing PT resources. Full article

Trehalose-6-phosphate phosphatase (TPP) modulates the Trehalose-6-phosphate–trehalose balance, a key regulatory node in plant carbon sensing and stress resilience. However, its functional roles in vegetative crops such as potato (Solanum tuberosum, St) remains poorly understood. Here, we conducted a genome-wide identification of the StTPP gene family and identified nine distinct loci distributed across five chromosomes. Phylogenetic analysis categorized these loci into three clades, supported by conserved HAD-box motifs and distinct exon–intron structures. Family expansion was driven by segmental duplication under purifying selection. In silico promoter analysis revealed cis-elements responsive to hormones, light, and stress, while network modeling identified 64 transcription factors potentially involved in regulating StTPP expression. A biphasic transcriptional response was observed in the salt-tolerant cultivar Xisen6: rapid induction of StTPP2/3/9 early in salt exposure, followed by late repression of most members. Subcellular localization assays indicated that StTPP3 is present in the nucleus and cytosol, suggesting multifunctional roles. These findings suggest that StTPPs integrate developmental and environmental signals, providing a molecular basis for improving potato stress tolerance and yield stability. Full article

Root-knot nematodes (RKNs) of the genus Meloidogyne spp., are among the most economically important groups of plant-parasitic nematodes worldwide, causing significant economic losses through yield reduction across a wide range of crops. In Croatia, although the presence of Meloidogyne spp. has been documented for decades, data at the species level was limited. As accurate identification is crucial for implementation of effective management strategies, we attempted to fill this gap. This study presents the results of a national survey of RKNs affecting potato crops as well as an early warning programme targeting vegetable crops, conducted across Croatia between 2022 and 2024. Nematodes were identified using morphological analyses (female perineal patterns and second-stage juveniles) and molecular methods (PCR with group-specific and species-specific primers, as well as DNA sequencing). Meloidogyne spp. were detected in 61 out of 210 samples, corresponding to an infestation rate of 29%. Four species were identified: M. incognita, M. hapla, M. arenaria, and M. javanica. Notably, M. incognita and M. javanica are reported here for the first time in Croatia. These results provide updated insights into the distribution and identity of RKNs in Croatia, thereby establishing a foundation for the implementation of sustainable management strategies. Full article

The accurate classification of plant diseases is vital for global food security, as diseases can cause major yield losses and threaten sustainable and precision agriculture. The classification of plant diseases in low-light noisy environments is crucial because crops can be continuously monitored even at night. Important visual cues of disease symptoms can be lost due to the degraded quality of images captured under low-illumination, resulting in poor performance of conventional plant disease classifiers. However, researchers have proposed various techniques for classifying plant diseases in daylight, and no studies have been conducted for low-light noisy environments. Therefore, we propose a novel model for classifying plant diseases from low-light noisy images called dilated pixel attention network (DPA-Net). DPA-Net uses a pixel attention mechanism and multi-layer dilated convolution with a high receptive field, which obtains essential features while highlighting the most relevant information under this challenging condition, allowing more accurate classification results. Additionally, we performed fractal dimension estimation on diseased and healthy leaves to analyze the structural irregularities and complexities. For the performance evaluation, experiments were conducted on two public datasets: the PlantVillage and Potato Leaf Disease datasets. In both datasets, the image resolution is 256 × 256 pixels in joint photographic experts group (JPG) format. For the first dataset, DPA-Net achieved an average accuracy of 92.11% and harmonic mean of precision and recall (F1-score) of 89.11%. For the second dataset, it achieved an average accuracy of 88.92% and an F1-score of 88.60%. These results revealed that the proposed method outperforms state-of-the-art methods. On the first dataset, our method achieved an improvement of 2.27% in average accuracy and 2.86% in F1-score compared to the baseline. Similarly, on the second dataset, it attained an improvement of 6.32% in average accuracy and 6.37% in F1-score over the baseline. In addition, we confirm that our method is effective with the real low-illumination dataset self-constructed by capturing images at 0 lux using a smartphone at night. This approach provides farmers with an affordable practical tool for early disease detection, which can support crop protection worldwide. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Potato leafroll virus (PLRV, species Polerovirus PLRV) is a major pathogen affecting potatoes worldwide. Since 2018, PLRV incidence has increased in Scottish potato crops. Deep sequencing of PLRV in Scottish potato plants revealed the prevalence of a novel PLRV type which became predominant in 2023, displacing the phylogenetically distinct variants that have been present in the region since at least 1989. Analysis of the infection dynamics of the cDNA clone-derived PLRV isolates in potato plants indicated that the novel PLRV may accumulate to higher levels compared to the historic one. Analysis of the genetic diversity of PLRV in early and late field generations (FGs) of seed potatoes showed a significantly reduced genetic diversity of the PLRV structural genes in the early FGs compared to the late FGs, while divergency of the non-structural genes remained similar across all FGs. Considering that late FGs are more likely to be infected with PLRV via tuber transmission, and early FGs via aphid transmission, these findings suggest that aphid transmission imposes a genetic bottleneck on the structural genes of PLRV, but not on its non-structural genes. Full article

Potatoes are a strategic crop in Poland, particularly important for agriculture in the southern and southeastern parts of the country. Environmental variability makes assessing yield stability and quality traits of varieties crucial for food security. Research Objective and Methodology: This three-year field study (2021–2023) aimed to comprehensively assess the yield stability and quality traits of mid-early potato varieties. The research was conducted in four pedologically diverse locations (rendzinas, brown soils, alluvial soils, and pseudopodzolic soils), according to the COBORU methodology. Key yield parameters (total and marketable tuber yield) and quality traits (dry-matter and starch content and yield) were analyzed. Interregional stability was also assessed. The environmental characteristics were supplemented with detailed analyses of soil physicochemical and biological properties, monitoring of agroclimatic parameters, and an assessment of the impact of geographical location. The collected data was subjected to advanced statistical analyses (ANOVA, correlations, descriptive statistics). Results analyses revealed significant yield variation across soil types, with the highest yields on alluvial soils and the lowest on pseudopodzolic soils. Geographic location significantly influenced yield stability, highlighting the role of local factors. Strong correlations were also found between soil properties and starch content (r = 0.61–0.73), indicating a key influence of the soil matrix on tuber quality. Conclusions and Recommendations: This study provides practical recommendations for selecting potato varieties adapted to specific soil types, precision fertilization strategies, and climate-change-adaptation protocols. Further research should focus on the impact of extreme weather events, optimized water management, and the use of precision agriculture. Full article

Slow-release nitrogen fertilizers enhance crop production and reduce environmental pollution, but their slow nitrogen release may cause insufficient nitrogen supply in the early stages of crop growth. Mixed nitrogen fertilization (MNF), combining slow-release nitrogen fertilizer with urea, is an effective way to increase yield and income and improve nitrogen fertilizer efficiency. This study used urea alone (Urea) and slow-release nitrogen fertilizer alone (C/SRF) as controls and employed meta-analysis and a random forest model to assess MNF effects on crop yield and nitrogen partial factor productivity (PFPN), and to identify key influencing factors. Results showed that compared with urea, MNF increased crop yield by 7.42% and PFPN by 8.20%, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 20 °C, and elevations of 750–1050 m; in soils with a pH of 5.5–6.5, where 150–240 kg·ha⁻¹ nitrogen with 25–35% content and an 80–100 day release period was applied, and the blending ratio was ≥0.3; and when planting rapeseed, maize, and cotton for 1–2 years. The top three influencing factors were crop type, nitrogen rate, and soil pH. Compared with C/SRF, MNF increased crop yield by 2.44% and had a non-significant increase in PFPN, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 5 °C, average annual precipitation ≤ 400 mm, and elevations of 300–900 m; in sandy soils with pH > 7.5, where 150–270 kg·ha⁻¹ nitrogen with 25–30% content and a 40–80 day release period was applied, and the blending ratio was 0.4–0.7; and when planting potatoes and rapeseed for 3 years. The top three influencing factors were nitrogen rate, crop type, and average annual precipitation. In conclusion, MNF should comprehensively consider crops, regions, soil, and management. This study provides a scientific basis for optimizing slow-release nitrogen fertilizers and promoting the large-scale application of MNF in farmland. Full article

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

Precise detection of potato diseases is critical for food security, yet traditional image segmentation methods struggle with challenges including uneven illumination, background noise, and the gradual color transitions of lesions under complex field conditions. Therefore, a collaborative segmentation framework of Otsu and Sobel edge detection based on the beluga whale optimization algorithm with a danger sensing mechanism (DSBWO) is proposed. The method introduces an S-shaped control parameter, a danger sensing mechanism, a dynamic foraging strategy, and an improved whale fall model to enhance global search ability, prevent premature convergence, and improve solution quality. DSBWO demonstrates superior optimization performance on the CEC2017 benchmark, with faster convergence and higher accuracy than other algorithms. Experiments on the Berkeley Segmentation Dataset and potato early/late blight images show that DSBWO achieves excellent segmentation performance across multiple evaluation metrics. Specifically, it reaches a maximum IoU of 0.8797, outperforming JSBWO (0.8482) and PSOSHO (0.8503), while maintaining competitive PSNR and SSIM values. Even under different Gaussian noise levels, DSBWO maintains stable segmentation accuracy and low CPU time, confirming its robustness. These findings suggest that DSBWO provides a reliable and efficient solution for automatic crop disease monitoring and can be extended to other smart agriculture applications. Full article

The potato is the third most important crop in the world, and more than 375 million metric tonnes of potatoes are produced globally on an annual basis. Potato Virus Y (PVY) poses a significant threat to the production of seed potatoes, resulting in economic losses and risks to food security. Current detection methods for PVY typically rely on serological assays for leaves and PCR for tubers; however, these processes are labor-intensive, time-consuming, and not scalable. In this proof-of-concept study, we propose the use of unmanned aerial vehicles (UAVs) integrated with hyperspectral cameras, including a downwelling irradiance sensor, to detect the PVY in commercial growers’ fields. We used a 400–1000 nm visible and near-infrared (Vis-NIR) hyperspectral camera and trained several standard machine learning and deep learning models with optimized hyperparameters on a curated dataset. The performance of the models is promising, with the convolutional neural network (CNN) achieving a recall of 0.831, reliably identifying the PVY-infected plants. Notably, UAV-based imaging maintained performance levels comparable to ground-based methods, supporting its practical viability. The hyperspectral camera captures a wide range of spectral bands, many of which are redundant in identifying the PVY. Our analysis identified five key spectral regions that are informative in identifying the PVY. Two of them are in the visible spectrum, two are in the near-infrared spectrum, and one is in the red-edge spectrum. This research shows that early-season PVY detection is feasible using UAV hyperspectral imaging, offering the potential to minimize economic and yield losses. It also highlights the most relevant spectral regions that carry the distinctive signatures of PVY. This research demonstrates the feasibility of early-season PVY detection using UAV hyperspectral imaging and provides guidance for developing cost-effective multispectral sensors tailored to this task. Full article

To explore the characteristics of soil microbial community structure diversity for different planting patterns in paddy fields, and to screen out the planting patterns suitable for the promotion of double-cropping rice areas in the middle reaches of the Yangtze River, five typical planting patterns were set up in this study. The five patterns are Chinese milk vetch–early rice–late rice (CRR, CK), Chinese milk vetch–early rice–sweet potato || late soybean (CRI), rapeseed–early rice–late rice (RRR), rapeseed–early rice–sweet potato || late soybean (RRI) and potato–early rice–late rice (PRR). The variation characteristics of soil microbial community structure diversity and the correlation between soil environmental factors and soil microbial community structure diversity under the triple-cropping system in the double-cropping rice area of the middle reaches of the Yangtze River were studied by 16S rRNA high-throughput sequencing and real-time fluorescence quantitative polymerase chain reaction (PCR). The results showed that after two years of experiment, the pH values of each treatment increased, and the rapeseed–early rice–late rice (RRR) model performed better. The soil organic matter and total nitrogen content of the milk vetch–early rice–sweet potato || late soybean (CRI) model was the highest, which increased by 7.89~35.02% and 6.59~26.80% compared with other treatments. The content of soil available phosphorus and available potassium in the potato–early rice–late rice (PRR) model was higher than that in other treatments, which was increased by 29.48% and 126.49% compared with the control. The Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) models were beneficial to increasing soil nitrate nitrogen and ammonium nitrogen content. Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–late rice (RRR) patterns were beneficial for improving the microbial diversity index. Proteobacteria, Chloroflexi, and Actinobacteria are the top three dominant phyla in terms of the relative abundance of soil bacteria, and the top three dominant fungi are Ascomycota, Basidiomycota, and Mucor. The Chinese milk vetch–early rice–sweet potato || late soybean (CRI) and rapeseed–early rice–sweet potato || late soybean (RRI) patterns increased the relative abundance of soil Actinobacteria and Ascomycota. The contents of ammonium nitrogen, total organic carbon, nitrate nitrogen, and available phosphorus were the main environmental factors affecting soil microbial community structure. The findings can provide references for screening out the planting patterns suitable for the promotion of double-cropping rice areas in the middle reaches of the Yangtze River. Full article

This study investigates the early detection of sweet potato scab by using hyperspectral imaging and machine learning techniques. The research focuses on developing an accurate, economical, and non-destructive approach for disease detection and grading. Hyperspectral imaging experiments were conducted on two sweet potato varieties: Guangshu 87 (resistant) and Guicaishu 2 (susceptible). Data preprocessing included denoising, region of interest (ROI) selection, and average spectrum extraction, followed by dimensionality reduction using principal component analysis (PCA) and random forest (RF) feature selection. A novel dynamic grading method based on spectral-time data was introduced to classify the early stages of the disease, including the early latent and early mild periods. This method identified significant temporal spectral changes, enabling a refined disease staging framework. Key wavebands associated with sweet potato scab were identified in the near-infrared range, including 801.8 nm, 769.8 nm, 898.5 nm, 796.4 nm, and 780.5 nm. Classification models, including K-nearest neighbor (KNN), support vector machine (SVM), and linear discriminant analysis (LDA), were constructed to evaluate the effectiveness of spectral features. Among these classification models, the MSC-PCA-SVM model demonstrated the best performance. Specifically, the Susceptible Variety Disease Classification Model achieved an overall accuracy (OA) of 98.65%, while the Combined Variety Disease Classification Model reached an OA of 95.38%. The results highlight the potential of hyperspectral imaging for early disease detection, particularly for non-destructive monitoring of resistant and susceptible sweet potato varieties. This study provides a practical method for early disease classification of sweet potato scab, and future research could focus on real-time disease monitoring to enhance sweet potato crop management. Full article