Search Results (611)

An advanced artificial intelligence (AI)-powered mobile automated impurity removal system was developed and integrated into potato harvesting machinery for decentralized agricultural environments in Japan. As opposed existing stationary AI systems in centralized processing facilities, this mobile prototype enables on-field impurity removal in real time through a systematic dual-evaluation methodology. The system integrates the YOLOX-small architecture with precision pneumatic actuators and achieves 40–50 FPS processing under dynamic field conditions. Algorithm validation across 10 morphologically diverse potato varieties (Danshaku, Harrow Moon, Hokkaikogane, Kitaakari, Kitahime, May Queen, Sayaka, Snowden, Snow March, and Toyoshiro) using count-based analysis showed exceptional recognition, with potato misclassification rates of 0.08 ± 0.03% (range: 0.01–0.32%) and impurity detection rates of 89.99 ± 1.25% (range: 80.00–93.30%). Cross-farm validation across seven commercial farms in Hokkaido confirmed robust algorithm consistency (PMR: 0.08 ± 0.03%, IDR: 90.56 ± 0.82%) without farm-specific calibration, establishing variety-independent and environment-independent operation. Field validation using weight-based analysis during actual harvesting at 1–4 km/h confirmed successful AI-to-field translation, with 0.22–0.42% potato misclassification and adaptive impurity removal of 71.43–85.29%. The system adapted intelligently, employing conservative sorting under high-impurity loads (71.43% removal, 0.33% misclassification) to prioritize potato preservation while maximizing efficiency under standard conditions (85.29% removal, 0.30% misclassification). The dual-evaluation framework successfully bridged the gap between AI accuracy in laboratory settings and effectiveness in agricultural operations. The proposed AI algorithm surpassed project targets for all tested conditions (>60% impurity removal, <1% potato misclassification). This successful integration demonstrates technical feasibility and commercial viability for widespread agricultural automation, with a validated 50% reduction in labor (four workers to two workers). This implementation provides a comprehensive validation methodology for next-generation autonomous harvesting systems. Full article

The root system of rice (Oryza sativa L.) is a central determinant of stress resilience and yield, functioning in resource acquisition, anchorage, and environmental sensing. This review synthesizes recent advances in understanding how the gaseous hormone ethylene acts as a master regulator to sculpt root system architecture by spatiotemporally integrating developmental cues and stress signals. We detail the core molecular machinery of ethylene in rice, encompassing its biosynthesis, perception, and signal transduction pathways. Ethylene modulates root development through intricate crosstalk with auxin, abscisic acid, and jasmonic acid, inhibiting primary root elongation while promoting lateral root initiation, adventitious rooting, root hair development, and aerenchyma formation. The review further dissects the context-dependent role of ethylene signaling in mediating adaptive responses to key abiotic stresses, including drought, hypoxia, salinity, and heavy metal stress. It also examines how ethylene influences root-microbe interactions, shaping the rhizosphere microbiome. Finally, we discuss root trait optimization strategies that leverage the ethylene signaling network, providing a mechanistic foundation for breeding next-generation rice varieties with enhanced stress tolerance and resource-use efficiency. Full article

Background/Objectives: Combining ability (CA) analysis is a key tool in maize breeding for developing superior hybrids by evaluating parental genetic potential through general combining ability (GCA) and specific combining ability (SCA). Despite its widespread use, knowledge of how CA techniques help overcome major constraints to maize production in sub-Saharan Africa (SSA) is limited. This review summarizes recent applications of CA analysis in addressing maize breeding challenges across SSA. Methods: A systematic literature search was conducted using ScienceDirect, Springer, and Google Scholar for studies published between 2020 and September 2025. Search terms included maize, combining ability, and SSA. The review followed PRISMA guidelines, and 94 studies met the eligibility criteria and were included in the analysis. Results: Most studies were conducted in Nigeria (42%), Ethiopia (16%), and Ghana (14%), indicating regional concentration of maize hybridization research within SSA. Yield improvement was the dominant breeding objective across the region. Inbred lines with high GCA were predominantly used as parental materials compared with open-pollinated varieties. The line × tester mating design was the most frequently applied, followed by other mating designs. Across 580 environments, GCA contributed 80%, SCA 19%, and combined GCA/SCA 1% to hybrid performance. The predominance of GCA across traits and environments underscores high additive gene effects, largely due to the high homozygosity of inbred line parents. Conclusions: It has been observed in this systematic review that combining ability analysis remains essential for enhancing maize productivity and resilience in SSA by enabling identification of superior parents, efficient mating designs, and development of widely adapted hybrids. Full article

Sugarcane (Saccharum spp. L.) is a globally vital sugar and energy crop whose genetic improvement has been constrained by its complex polyploid–allopolyploid genome. To address this limitation, we developed a practical, high-throughput single-nucleotide polymorphism (SNP) genotyping system. Using specific-locus amplified fragment sequencing (SLAF-seq) on 107 diverse accessions, we identified 2,420,550 high-quality SNPs anchored to the Saccharum officinarum LA-Purple genome. Stringent filtering yielded 55,750 SNPs for population analysis, which revealed three distinct genetic groups consistent with breeding history and adaptation. From these resources, 329 SNPs were converted into PCR-based ligase detection reaction (PCR-LDR) markers, resulting in a validated panel of 177 highly reliable SNPs (151 core and 26 extended) organized into an efficient multiplex typing system. The panel exhibited exceptional discriminatory power, successfully distinguishing all 303 tested sugarcane varieties and clearly resolving 186 individuals from three segregated hybrid populations. Compared to existing SSR and SNaPshot platforms, this SNP system offers superior experimental reproducibility, enhanced varietal clustering, and broader genome coverage. This work provides a robust, efficient genotyping tool to advance sugarcane variety identification, germplasm management, pedigree analysis, and marker-assisted breeding, with potential applicability to other complex polyploid crops. Full article

Intelligent fruit-picking robots have emerged as a promising solution to labor shortages and the increasing costs of manual harvesting. This review provides a systematic and critical overview of recent advances in three core domains: (i) vision-based fruit and peduncle detection, (ii) motion planning and obstacle-aware navigation, and (iii) robotic manipulation technologies for diverse fruit types. We summarize the evolution of deep learning-based perception models, highlighting improvements in occlusion robustness, 3D localization accuracy, and real-time performance. Various planning frameworks—from classical search algorithms to optimization-driven and swarm-intelligent methods—are compared in terms of efficiency and adaptability in unstructured orchard environments. Developments in multi-DOF manipulators, soft and adaptive grippers, and end-effector control strategies are also examined. Despite these advances, critical challenges remain, including heavy dependence on large annotated datasets; sensitivity to illumination and foliage occlusion; limited generalization across fruit varieties; and the difficulty of integrating perception, planning, and manipulation into reliable field-ready systems. Finally, this review outlines emerging research trends such as lightweight multimodal networks, deformable-object manipulation, embodied intelligence, and system-level optimization, offering a forward-looking perspective for autonomous harvesting technologies. Full article

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. Full article

The introduction of Super-High-Density (SHD) olive orchards represents a crucial innovation in modern olive growing, enhancing sustainability. However, the long-term success of these planting systems depends strongly on cultivar selection, combining suitable vegetative and reproductive traits. This three-year field study investigated key floral biology parameters—flowering phenograms, gynosterility, and self-compatibility—of ten olive cultivars grown under irrigated conditions in southern Italy: ‘Arbequina’, ‘Arbosana’, ‘Cima di Bitonto’, ‘Coratina’, ‘Don Carlo’, ‘Frantoio’, ‘Favolosa’ (=‘Fs-17’), ‘I-77’, ‘Koroneiki’, and ‘Urano’ (=‘Tosca’). Flowering phenograms varied significantly across years and cultivars, showing temporal shifts related to chilling accumulation and yield of the previous year. Early blooming cultivars (‘Arbequina’, ‘Arbosana’, and ‘Coratina’) exhibited partial flowering overlap with mid-season ones, enhancing cross-pollination opportunities. Quantitative analysis of flowering overlap revealed that most cultivar combinations exceeded the 70% threshold required for effective pollination, although specific genotypes (‘Coratina’, ‘Fs-17’, and especially ‘I-77’) showed critical mismatches, while ‘Frantoio’ and ‘Arbequina’ emerged as the most reliable pollinizers. Gynosterility exhibited statistical differences among cultivars and canopy positions: ‘I-77’ showed the highest values (71.4%), while ‘Coratina’ and ‘Cima di Bitonto’ showed the lowest ones (7.3 and 8.4%, respectively). The median portions of the canopies generally displayed a greater number of sterile flowers (29.4%), reflecting the combined effect of genetic and environmental factors such as light exposure. In the inflorescence, the majority of gynosterile flowers were concentrated in the lower part, for all canopy portions (modal value). Self-compatibility tests were performed considering a fruit set of 1% as a threshold to discriminate. For open pollination, the fruit set was highly variable among cultivars, ranging from 0.5% in ‘I-77’ to 4.7% in ‘Arbosana’. Apart from ‘I77’, all varieties achieved a fruit set greater than 1%. Instead, for the self-pollination, only ‘Arbequina’, ‘Koroneiki’, ‘Frantoio’, and ‘Cima di Bitonto’ could be identified as pseudo-self-compatible, whereas ‘Coratina’, ‘Fs-17’, and the others were clearly self-incompatible and therefore unsuitable for monovarietal orchards in areas with limited availability of pollen. By integrating self-compatibility and gynosterility data, the cultivars were ranked according to reproductive aptitude, identifying ‘Cima di Bitonto’ and ‘Frantoio’ as the most fertile genotypes, whereas ‘Don Carlo’ and particularly ‘I-77’ showed severe genetic sterility constraints. The findings underline the critical role of floral biology in defining reproductive efficiency and varietal adaptability in SHD systems. This research provides valuable insights for optimizing cultivar selection, orchard design, and management practices, contributing to the development of sustainable, climate-resilient olive production models for Mediterranean environments. Full article

Background: Peanut is susceptible to iron (Fe) deficiency, particularly in calcareous soils. However, comparative studies on the adaptive mechanisms of different peanut cultivars to Fe deficiency remain limited. This study aimed to investigate the physiological and molecular responses of two distinct peanut cultivars to Fe deprivation and to identify the key traits contributing to differential Fe efficiency. Methods: Two peanut cultivars, LH11 and YZ9102, were cultivated under Fe-sufficient and Fe-deficient conditions, using both hydroponic and pot-based soil culture systems. Multiple parameters were assessed, including visual symptomology, biomass, tissue Fe concentration, active Fe in leaves, chlorophyll (Chl) content (SPAD value), net photosynthetic rate (Pn), Chl fluorescence (Fv/Fm), rhizosphere pH, root ferric chelate reductase (FCR) activity, and the relative expression of two Fe-acquisition-related genes (AhIRT1 and AhFRO1) via qRT-PCR. Results: Cultivar YZ9102 exhibited more severe Fe deficiency chlorosis symptoms, which also appeared earlier than in LH11, under both cultivation systems. Under Fe deficiency, YZ9102 showed significantly lower Chl content, Pn, and Fv/Fm compared to LH11. In contrast, LH11 demonstrated a greater capacity for rhizosphere acidification and maintained significantly higher root FCR activity under Fe-limited conditions. Gene expression analysis revealed that Fe deficiency induced the up-regulation of AhIRT1 and AhFRO1 in the roots of LH11, while their transcript levels were suppressed or unchanged in YZ9102. Conclusions: The peanut cultivar LH11 possesses superior tolerance to Fe deficiency compared to YZ9102. This enhanced tolerance is attributed to a synergistic combination of traits: the maintenance of photosynthetic performance, efficient rhizosphere acidification, heightened root Fe³⁺ reduction capacity, and the positive transcriptional regulation of key Fe uptake genes. These findings provide crucial insights for the selection and breeding of Fe-efficient peanut varieties for cultivation in Fe-deficient environments. Full article

Maize (Zea mays L.), a vital crop for global food and economic security, faces intensifying biotic and abiotic stresses driven by climate change, including drought, heat, and erratic rainfall. This review synthesizes emerging biotechnology-driven strategies designed to enhance maize resilience under these shifting environmental conditions. We present an integrated framework that encompasses CRISPR/Cas9 and next-generation genome editing, Genomic Selection (GS), Environmental Genomic Selection (EGS), and multi-omics platforms—spanning transcriptomics, proteomics, metabolomics, and epigenomics. These approaches have significantly deepened our understanding of complex stress-adaptive traits and genotype-by-environment interactions, revealing precise targets for breeding climate-resilient cultivars. Furthermore, we highlight enabling technologies such as high-throughput phenotyping, artificial intelligence (AI), and nanoparticle-based gene delivery—including novel in planta and transformation-free protocols—that are accelerating translational breeding. Despite these technical breakthroughs, barriers such as genotype-dependent transformation efficiency, regulatory landscapes, and implementation costs in resource-limited settings remain. Bridging the gap between laboratory innovation and field deployment will require coordinated policy support and global collaboration. By integrating molecular breakthroughs with practical deployment strategies, this review offers a comprehensive roadmap for developing sustainable, climate-resilient maize varieties to meet future agricultural demands. Full article

Advances in plant nutrition have driven substantial progress in modern fertilization technologies. Nevertheless, excessive chemical fertilizer application, low nutrient-use efficiency, and the resulting environmental pollution remain widespread. We have reviewed the research progress and existing limitations in the field of plant nutrition and fertilization technology. Based on the traditional plant nutrition diagnosis and integrating visual diagnosis methods, this study explores the intrinsic relationship between plant growth status, nutrient supply conditions, and crop yield and proposed the concept of “status nutrition”. Variations in environmental nutrient conditions lead plants to exhibit distinct growth status in terms of vigor and phenotype. We define the plant nutritional status reflected by this growth status as “status nutrition”. Based on growth characteristics, plant growth status can be classified as weak, normal, or vigorous, corresponding to deficient, appropriate, and excessive environmental nutrient supply, respectively. Guided by this concept, an innovative rice “extremely dense planting” technology is integrated by increasing planting density, eliminating tiller-stage fertilization, and optimizing nitrogen management. The technology adapts to growth status with low nutrient demand, coordinates population growth and main-stem panicle formation, and achieves high yield with reduced fertilizer inputs. Further research is needed on the nutrient metabolism mechanisms of plants under different growth statuses and the growth status grading system. The promotion of “extremely dense planting” is constrained by crop variety traits and soil fertility, and its parameters urgently need to be optimized. Overall, the framework of “status nutrition” provides important theoretical support for the development and application of crop high-yield cultivation technologies. Full article

Soybean (Glycine max L.) is an essential food and economic crop in China, yet its growth and yield are severely constrained by saline–alkali stress. A saline–alkali soil exacerbates root absorption barriers, leading to 30–50% yield losses. Understanding the mechanisms underlying alkali tolerance is therefore crucial for developing stress-resilient soybean varieties and improving the productivity of saline–alkali land. In our previous study, we evaluated 99 soybean germplasms from Northeast China and obtained the alkali-tolerant varieties HN48 and HN69, along with the alkali-sensitive varieties HNWD4 and HN83. In this study, fifteen-day-old soybean seedlings were subjected to (30 mM NaHCO₃) alkali stress for 72 h, and whole plants were sampled to assess their morphology and physiology, while leaf tissues were harvested for biochemical analysis. For transcriptomic analysis, soybean seedlings were exposed to alkali stress (50 mM NaHCO₃, pH 9.0) for 6 h, and leaf and root tissues were harvested for RNA sequencing. The results showed that alkali-tolerant varieties mitigated these effects by suppressing excessive ROS generation by 55–63%, decreasing malondialdehyde (MDA) accumulation by 37–39%, and increasing photosynthetic efficiency by 18.3%, as well as accumulating more osmoprotectants and activating antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT) under alkaline stress. Transcriptome analysis showed that the alkali-tolerant variety HN69 exhibited cultivar-specific enrichment of metabolism cytochrome P450, estrogen signaling, and GnRH signaling pathways under alkali stress. These results collectively indicate that alkali-tolerant soybean varieties adapt to alkali stress through coordinated multi-pathway responses, with differential pathway enrichment potentially underlying the variation in alkali tolerance between cultivars. Overall, this study elucidates the physiological and molecular mechanisms of alkali tolerance in soybean, providing a theoretical foundation for breeding stress-tolerant germplasms. Full article

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

Against the backdrop of the continuous expansion of the global aquaculture industry and the growing demand for high-quality feed protein, the development of sustainable alternative protein sources to fishmeal is crucial. Cottonseed protein, particularly cottonseed protein concentrate, has emerged as a highly promising plant-based alternative raw material due to its high protein content and cost advantages. This review systematically evaluates the application effects, challenges, and mechanisms of action of cottonseed protein in fish feed. Core analysis indicates that the primary limiting factor of cottonseed protein is the antinutritional factor free gossypol. High-level replacement (typically >30%) of fishmeal can inhibit fish growth, reduce protein deposition, and impair intestinal health. These adverse effects are closely associated with the downregulation of the hepatic mTOR signaling pathway—a central regulator of protein synthesis and cell growth—shifting the organism’s energy allocation from growth to stress adaptation. Furthermore, the unique fatty acid profile of cottonseed protein may exacerbate energy metabolism imbalance. To overcome gossypol toxicity, physical, chemical, and biological detoxification technologies have been widely applied. Among these, biological methods (such as Bacillus subtilis fermentation and CotA laccase-catalyzed degradation) are particularly outstanding, not only efficiently removing gossypol (removal rate > 90%) but also degrading macromolecular proteins into more digestible and absorbable small peptides and amino acids, significantly enhancing the nutritional value of cottonseed protein. Although the application prospects for cottonseed protein are broad, gaps remain in current research, particularly concerning the deeper metabolic pathways, nutrient utilization efficiency, and long-term impacts on metabolic homeostasis of detoxified cottonseed protein in fish. Future research needs to employ molecular nutrition and multi-omics technologies to elucidate its metabolic mechanisms and optimize detoxification processes and precision feeding strategies. Glandless cottonseed varieties, which fundamentally address the gossypol issue, are considered the most transformative development direction. Through continuous technological innovation, cottonseed protein is expected to become a core feed protein ingredient promoting the sustainable development of the global aquaculture industry. Full article

The System of Rice Intensification which promotes agro-ecological practices like alternate wetting and drying (AWD) to enhance root growth and resource efficiency, relies on the genotypic capacity of rice varieties to undergo physiological adaptation. This study elucidates the molecular basis of such adaptation by investigating the transcriptomic profile of four rice varieties to continuous flooding (CF) and AWD at 50 days after transplanting. Our analysis revealed distinct, organ-specific acclimation strategies. Roots underwent extensive transcriptional reprogramming, underscoring their role as the primary site of plasticity. Under CF, a conserved response involving cell wall reinforcement was accompanied by variety-specific strategies, ranging from sustained growth to enhanced anaerobic metabolism. Under AWD, roots shifted toward water stress management, with varieties employing distinct defensive (e.g., diterpenoid biosynthesis) and metabolic programs. Associated transcription factors (TFs) enriched under CF included Dof and MYB, whereas bZIP, HSF, and WRKY factors predominated under AWD. In leaves, acclimation to AWD involved more targeted adjustments, including modulation of nitric oxide signaling and photoprotective pathways, regulated by TFs such as WRKY, NAC, and HSF. Varieties with robust TF responses, such as IR64 and Hitachi hatamochi, showed comprehensive regulatory shifts, while others exhibited more constrained profiles. Overall, this study provides a molecular framework for understanding variety-specific adaptation to SRI-relevant water management practices and identifies key TFs as promising candidates for breeding climate-resilient rice. Full article

Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited labels and dataset bias. Real-field complexities, such as environmental variability, heterogeneous varieties or temporal dynamics as are often overlooked. Numerous studies have been conducted to address these challenges, proposing advanced learning strategies and improved evaluation protocols. Synthetic data generation and self-supervised learning reduce dataset bias, while domain adaptation, hyperspectral, and thermal signals improve robustness across sites. However, a large portion of current methods are developed and validated mainly on clean laboratory datasets, which do not capture the variability of real-field conditions. Existing AI models often lead to imperfect detection results when dealing with field images complexities, such as dense vegetation, variable illumination or changing symptom expression. Although augmentation techniques can approximate real-world conditions, incorporating field data represents a substantial enhancement in model reliability. Federated transfer learning offers a promising approach to enhance plant disease detection, by enabling collaborative training of models across diverse agricultural environments, using in-field data but without disclosing the participants data to each others. In this study, we collaboratively trained a hybrid Graph–SNN model using federated learning (FL) to preserve data privacy, optimized for efficient use of participant resources. The model achieved an accuracy of 0.9445 on clean laboratory data and 0.6202 exclusively on field data, underscoring the considerable challenges posed by real-world conditions. Our findings demonstrate the potential of FL for privacy preserving and reliable plant disease detection under real field conditions. Full article