Search Results (3,318)

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

Hazelnut is an important crop worldwide, and the characteristics of the fruits are quite variable according to a number of factors, including variety and cultivation conditions, which in turn can vary according to harvest year. This study aimed to investigate some physical and chemical characteristics of three hazelnut varieties grown in Portugal (Grada de Viseu, Tonda di Giffoni and Butler) along two different harvesting years (2021 and 2022). Also, the microbial quality was investigated for its relevance to the conservation of the fruits. The physical properties evaluated were biometric characteristics and colour, the chemical components analysed were moisture, lipids, protein, ash and fibre, and the microbial properties investigated were the microorganisms, moulds and yeasts. The results showed that, generically, statistically significant differences were found between the three varieties under study on several properties investigated. The kernel was confirmed as the lighter part of all hazelnuts, and when comparing between varieties, Tonda di Giffoni presented the lighter fruits in both harvesting years. With respect to weight, the Tonda di Giffoni variety was the lightest in both harvest years. Moisture content was observed to be higher than the recommended limits for two of the samples (Grada de Viseu in 2021: 6.01 ± 0.26 g/100 g and Butler in 2022: 6.02 ± 0.37 g/100 g), although the difference was marginal given that the recommended limit is 6%. Not surprisingly, lipids were the major chemical component, ranging from 66.46 ± 1.67 to 70.14 ± 1.75 g/100 g in 2021 and from 64.38 ± 1.67 to 77.77 g/100 g in 2022. It was further observed that the three varieties presented a satisfactory microbiological quality. Finally, applying factor analysis with principal components and Varimax rotation, a solution that explains 92.8% of the variance was obtained. This study provided information that is relevant for the characterisation and evaluation of variability according to the year of hazelnuts of three varieties cultivated in Portugal. Full article

Water deficiency and low water use efficiency severely constrain wheat yield in dryland regions. This study aimed to identify suitable tillage methods and straw management to improve dry matter production, grain yield, and water use efficiency of wheat in the dryland winter wheat–summer bean (hereafter referred to as wheat-soybean) double-cropping system. A long-term located field experiment (onset in October 2009) with two tillage methods—plowing (PT) and rotary tillage (RT)—and two straw management—no straw mulching (NS) and straw mulching (SM)—was conducted at a typical dryland in China. The wheat yield and yield component, dry matter accumulation and translocation characteristics, and water use efficiency were investigated from 2014 to 2018. Straw management significantly affected wheat yield and yield components, while tillage methods had no significant effect. Furthermore, the interaction of tillage methods and straw management significantly affected yield and yield components except for the spike number. RTSM significantly increased the spike number, grains per spike, 1000-grain weight, harvest index, and grain yield by 12.5%, 8.4%, 6.0%, 3.4%, and 13.4%, respectively, compared to PTNS. Likewise, RTSM significantly increased the aforementioned indicators by 14.8%, 10.1%, 7.5%, 3.6%, and 20.5%, compared to RTNS. Mechanistic analysis revealed that, compared to NS, SM not only significantly enhanced pre-anthesis and post-anthesis dry matter accumulation, and pre-anthesis dry matter tanslocation to grain, but also significantly improved pre-sowing water storage, water consumption during wheat growth, water use efficiency, and water-saving for produced per kg grain yield, with the greatest improvements obtained under RT than PT. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis confirmed RTSM’s yield superiority was mainly ascribed to straw-induced improvements in dry matter and water productivity. In a word, rotary tillage with straw mulching could be recommended as a suitable practice for high-yield wheat production in a dryland wheat-soybean double-cropping system. Full article

Chickpea is a highly weed-prone crop with limited herbicide options and high labor demands, raising the following question: Can fall-planted legume–cereal cover crops (CCs) improve soil properties while reducing herbicide use and manual weeding pressure? To explore this, we evaluated the effect of fall-planted winter rye (WR) alone in 2021 and mixed with hairy vetch (HV) in 2022 and 2023 at Randolph farm in Petersburg, Virginia. The objectives were two-fold: (a) to examine the effect of CCs on soil properties using monthly growth dynamics and biomass harvested from fifteen 0.25 m²-quadrants and (b) to evaluate the efficiency of five termination methods: (1) green manure (GM); (2) GM plus pre-emergence herbicide (GMH); (3) burn (BOH); (4) crimp mulch (CRM); and (5) mow-mulch (MW) in suppressing weeds in chickpea fields. Weed distribution, particularly nutsedge, was patchy and dominant on the eastern side. Growth dynamics followed an exponential growth rate in fall 2022 (R² ≥ 0.994, p < 0.0002) and a three-parameter sigmoidal curve in 2023 (R² ≥ 0.972, p < 0.0047). Biomass averaged 55.8 and 96.9 t/ha for 2022 and 2023, respectively. GMH consistently outperformed GM in weed suppression, though GM was not significantly different from no-till systems by the season’s end. Kabuli-type chickpeas under GMH had significantly higher yields than desi types. Pooled data fitted well to a three-parametric logistic curve, predicting half-time to 50% weed coverage at 35 (MM), 38 (CRM), 40 (BOH), 46 (GM), and 53 (GMH) days. Relapses of CCs were consistent in no-till systems, especially BOH and MW. Although soil properties improved, CCs alone did not significantly suppress weed. Full article

This study explores the revival of cotton (Gossypium spp. L.) farming in Italy through sustainable practices, addressing economic and water-related challenges by integrating cultivar selection, arbuscular mycorrhizal fungi (AMF) inoculation, and deficit irrigation under organic farming. Field trials evaluated two widely grown Mediterranean cultivars (Armonia and ST-318) under three irrigation levels (I-100: 100% crop water requirement; I-70: 70%; I-30: 30%) across two Sicilian soil types (sandy loam vs. clay-rich). Under I-100, lint yields reached 0.99 t ha⁻¹, while severe deficit (I-30) yielded only 0.40 t ha⁻¹. However, moderate deficit (I-70) maintained 75–79% of full yields, proving a viable strategy. AMF inoculation significantly enhanced plant height (68.52 cm vs. 65.85 cm), boll number (+22.1%), and seed yield (+12.5%) (p < 0.001). Cultivar responses differed: Armonia performed better under water stress, while ST-318 thrived with full irrigation. Site 1, with higher organic matter, required 31–38% less water and achieved superior irrigation water productivity (1.43 kg m⁻³). Water stress also shortened phenological stages, allowing earlier harvests—important for avoiding autumn rains. These results highlight the potential of combining adaptive irrigation, resilient cultivars, and AMF to restore sustainable cotton production in the Mediterranean, emphasizing the importance of soil-specific management. Full article

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

The objective of this study was to evaluate the efficacy of selected herbicides permitted for use in sustainable agriculture, specifically targeting spring rye and spring barley in a no-till farming system. The application of chemical herbicide protection in the cultivation of spring rye and barley significantly increased the yield and improved the quality parameters of the harvested grain, with the most pronounced effect observed in spring barley. The effectiveness of the herbicide treatment in reducing the number of weeds was 99.4% for spring rye and 82.39% for spring barley. The study demonstrated that the application of chemical herbicide protection had a positive impact on the quality parameters of spring barley grain. Both the thousand-grain weight and protein content were significantly higher in the grain collected from protected plots compared to the control plots. By utilizing herbicides permitted for use in integrated production (IP) in a sustainable manner, we protect the environment while minimizing the impact on crop yields and maintaining the quality of the harvested produce. Full article

Enhancing the yield and qualitative traits of horticultural crops without further hampering the environment constitutes an urgent challenge that could be addressed by implementing innovative agronomic tools, such as plant biostimulants. This study investigated the effects of three commercial biostimulants—BIO1 (fulvic/humic acids), BIO2 (leonardite-humic acids), and BIO3 (plant-based extracts)—on leaf ecophysiology, yield, and fruit quality in two raspberry cultivars, ‘Autumn Bliss’ (AB) and ‘Zeva’ (Z), grown in an open-field context, to assess their effectiveness in raspberry cultivation. Experimental activities involved two Research Years (RYs), namely, year 2023 (RY 1) and 2024 (RY 2). Leaf parameters such as chlorophyll, flavonols, anthocyanins, and the Nitrogen Balance Index (NBI) were predominantly influenced by the interaction between Treatment, Year and Cultivar factors, indicating context-dependent responses rather than direct biostimulant effects. BIO2 showed a tendency to increase yield (g plant⁻¹) and berry number plant⁻¹, particularly in RY 2 (417.50 g plant⁻¹, +33.93% vs. control). Fruit quality responses were cultivar and time-specific: BIO3 improved soluble solid content in AB (12.8 °Brix, RY 2, Intermediate Harvest) and Z (11.43 °Brix, +13.91% vs. BIO2). BIO2 reduced titratable acidity in AB (3.12 g L⁻¹) and increased pH in Z (3.02, RY 2) but also decreased °Brix in Z. These findings highlight the potential of biostimulants to modulate raspberry physiology and productivity but underscore the critical role of cultivar, environmental conditions, and specific biostimulant composition in determining the outcomes, which were found to critically depend on tailored application strategies. Full article

Reducing post-harvest losses of cereal crops is a key challenge for ensuring global food security amid the limited arable land and growing population. This study investigates the effectiveness of electron beam irradiation (5 MeV, ILU-10 accelerator) as a physical decontamination method for various cereal crops cultivated in Kazakhstan. Samples were irradiated at doses ranging from 1 to 5 kGy, and microbiological indicators—including Quantity of Mesophilic Aerobic and Facultative Anaerobic Microorganisms (QMAFAnM), yeasts, and molds—were quantified according to national standards. Experimental results demonstrated an exponential decline in microbial contamination, with a >99% reduction achieved at doses of 4–5 kGy. The modeled inactivation kinetics showed strong agreement with the experimental data: R² = 0.995 for QMAFAnM and R² = 0.948 for mold, confirming the reliability of the exponential decay models. Additionally, key quality parameters—including protein content, moisture, and gluten—were evaluated post-irradiation. The results showed that protein levels remained largely stable across all doses, while slight but statistically insignificant fluctuations were observed in moisture and gluten contents. Principal component analysis and scatterplot matrix visualization confirmed clustering patterns related to radiation dose and crop type. The findings substantiate the feasibility of electron beam treatment as a scalable and safe technology for improving the microbiological quality and storage stability of cereal crops. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Drosophila suzukii is an invasive pest of many fruit crops worldwide. Employing the Sterile Insect Technique (SIT) could mitigate D. suzukii population growth and crop damage. This study evaluated the efficacy of SIT on commercial fruit, by (1) validating the quality of irradiated sterile males (male mating competitiveness, courtship, and flight performance) in the laboratory, and (2) assessing population suppression and fruit damage reduction in commercial raspberry fields. Treatment with SIT was compared to the grower’s standard chemical insecticide program throughout the season. The principal metrics of efficacy were trap counts of wild adult female D. suzukii in crops and larvae per fruit during harvesting. These metrics together with monitoring of border areas allowed targeting of high-pressure areas with higher releases of sterile males, to maximise efficacy for a given release number. The sterile male D. suzukii were as competitive as their fertile non-irradiated counterparts in laboratory mating competitiveness and flight performance studies while fertility egg-to-pupae recovery was reduced by 99%. In commercial raspberry crops, season-long releases of sterile males significantly suppressed the wild D. suzukii population, compared to the grower standard control strategy; with up to 89% reduction in wild female D. suzukii and 80% decrease in numbers of larvae per harvested fruit. Additionally, relative fruit waste (i.e., percentage of harvested fruits rejected for sale) at harvest was reduced for early, mid and late harvest crops, by up to 58% compared to the grower standard control. SIT has the potential to provide an effective and sustainable strategy for managing D. suzukii in raspberries, increasing marketable yield by reducing adult populations, fruit damage and waste fruit. SIT could therefore serve as a valuable tool for integrated pest management practices in berry production systems. Full article

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning. Full article