Search Results (32)

The accurate assessment of water availability throughout the cassava cropping season (the initial, developmental, mid-season, and late stages) is crucial for mitigating the impacts of climate change on crop production. Using the Mann–Kendall Test, we investigated the trends in rainfall and cassava crop evapotranspiration (ETc) within the Cross River basin in Nigeria. Reference evapotranspiration (ETo) was based on two approaches, namely Artificial Neural Network (ANN) modelling and three established empirical models—the Penman–Monteith (considered the standard method), Blaney–Morin–Nigeria (BMN), and Hargreaves–Samani (HAG) models. ANN predictions were performed by using inputs from BMN and HAG parameters, denoted as BMN-ANN and HAG-ANN, respectively. The results from the ANN models were compared to those obtained from the Penman–Monteith method. Remotely sensed meteorological data spanning 39 years (1979–2017) were acquired from the Climatic Research Unit (CRU) to estimate ETc, while cassava yield data were acquired from the International Institute of Tropical Agriculture (IITA), Ibadan. The study revealed a significant upward trend in cassava crop ETc over the study period. Additionally, the ANN models outperformed the empirical models in terms of prediction accuracy. The BMN-ANN model with a Tansig activation function and a 3-3-1 architecture (number of input neurons, hidden layers, and output neurons) achieved the highest performance, with a coefficient of determination (R²) of 0.9890, a root mean square error (RMSE) of 0.000056 mm/day, and a Willmott’s index of agreement (d) of 0.9960. There is a decreasing trend in cassava yield in the region and further analysis indicated potential average daily water deficits of approximately −1.1 mm/day during the developmental stage. These deficits could potentially hinder root biomass, yield, and overall cassava yield in the Cross River basin. Our findings highlight the effectiveness of ANN modelling for irrigation planning, especially in the face of a worsening climate change scenario. Full article

As a crop with significant medicinal value and nutritional components, the market demand for bitter melon continues to grow. The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm resources crucial for breeding work. To address the limitations of time-consuming and less accurate traditional manual identification methods, there is a need to enhance the automation and intelligence of bitter melon phenotype detection. This study developed a bitter melon phenotype detection model named CSW-YOLO. By incorporating the ConvNeXt V2 module to replace the backbone network of YOLOv8, the model’s focus on critical target features is enhanced. Additionally, the SimAM attention mechanism was introduced to compute attention weights for neurons without increasing the parameter count, further enhancing the model’s recognition accuracy. Finally, WIoUv3 was introduced as the bounding box loss function to improve the model’s convergence speed and positioning capabilities. The model was trained and tested on a bitter melon image dataset, achieving a precision of 94.6%, a recall of 80.6%, a mAP50 of 96.7%, and an F1 score of 87.04%. These results represent improvements of 8.5%, 0.4%, 11.1%, and 4% in precision, recall, mAP50, and F1 score, respectively, over the original YOLOv8 model. Furthermore, the effectiveness of the improvements was validated through heatmap analysis and ablation experiments, demonstrating that the CSW-YOLO model can more accurately focus on target features, reduce false detection rates, and enhance generalization capabilities. Comparative tests with various mainstream deep learning models also proved the superior performance of CSW-YOLO in bitter melon phenotype detection tasks. This research provides an accurate and reliable method for bitter melon phenotype identification and also offers technical support for the visual detection technologies of other agricultural products. Full article

Neonicotinoids are synthetic, nicotine-derived insecticides used worldwide to protect crops and domestic animals from pest insects. The reported evidence shows that they are also able to interact with mammalian nicotine receptors (nAChRs), triggering detrimental responses in cultured neurons. Exposure to high neonicotinoid levels during the fetal period induces neurotoxicity in animal models. Considering the persistent exposure to these insecticides and the key role of nAChRs in brain development, their potential neurotoxicity on mammal central nervous system (CNS) needs further investigations. We studied here the neurodevelopmental effects of different generations of neonicotinoids on CNS cells in mouse fetal brain and primary cultures and in neuronal cells and organoids obtained from human induced pluripotent stem cells (iPSC). Neonicotinoids significantly affect neuron viability, with imidacloprid (IMI) inducing relevant alterations in synaptic protein expression, neurofilament structures, and microglia activation in vitro, and in the brain of prenatally exposed mouse fetuses. IMI induces neurotoxic effects also on developing human iPSC-derived neurons and cortical organoids. Collectively, the current findings show that neonicotinoids might induce impairment during neuro/immune-development in mouse and human CNS cells and provide new insights in the characterization of risk for the exposure to this class of pesticides. Full article

Infections with phytoplasma present one of the most significant biotic stresses influencing plant health, growth, and production. The phytoplasma ‘Candidatus Phytoplasma solani’ infects a variety of plant species. This pathogen impacts the physiological and morphological characteristics of plants causing stunting, yellowing, leaf curling, and other symptoms that can lead to significant economic losses. The aim of this study was to determine biochemical changes in peony (Paeonia tenuifolia L.), mint (Mentha × piperita L.), and dill (Anethum graveolens L.) induced by ‘Ca. Phytoplasma solani’ in Serbia as well as to predict the impact of the biotic stress using artificial neural network (ANN) modeling. The phylogenetic position of the Serbian ‘Ca. Phytoplasma solani’ strains originated from the tested hosts using 16S rRNA (peony and carrot strains) and plsC (mint and dill strains) sequences indicated by their genetic homogeneity despite the host of origin. Biochemical parameters significantly differed in asymptomatic and symptomatic plants, except for total anthocyanidins contents in dill and the capacity of peony and mint extracts to neutralize superoxide anions and hydroxyl radicals, respectively. Principal Component Analysis (PCA) showed a correlation between different chemical parameters and revealed a clear separation among the samples. Based on the ANN performance, the optimal number of hidden neurons for the calculation of TS, RG, PAL, LP, NBT, ^•OH, TP, TT, Tflav, Tpro, Tant, DPPH, and Car was nine (using MLP 8-9-13), as it produced high r² values (1.000 during the training period) and low SOS values. Developing an effective early warning system for the detection of plant diseases in different plant species is critical for improving crop yield and quality. Full article

Nitrogen-based fertilizers represent the most common fertilization tools, particularly used in crop food agriculture, despite the low cost-efficiency and the high negative environmental impact. At present, there is still inadequate information available about the effects of urea on human health; nevertheless, previous studies in animals observed that high urea concentration exposure can damage different tissues, including the brain. In several vertebrates, a crucial factor involved in neuronal cell formation is represented by the gas molecule, nitric oxide (NO), derived from the conversion of arginine to citrulline through the enzymatic activity of nitric oxide synthases (NOS). In zebrafish, three different isoforms of the NOS gene are known: nos1, nos2a, and nos2b. In the present study we show that nos1 represents the unique isoform with a stable high expression in the brain and spinal cord during all the embryonic stages of zebrafish development. Then, by using a specific transgenic zebrafish line, Tg(HuC:GFP), to mark neuronal cells, we observed nos1 to be specifically expressed in neurons. Interestingly, we observed that urea exposure at sub-lethal doses affected cell proliferation and the number of nos1-expressing cells, inducing apoptosis. Consistently, brain NO levels were observed to be reduced in urea-treated animals compared to untreated ones. This finding represents the first evidence that urea exposure affects the expression of a key gene involved in neuronal cell formation during embryonic development. Full article

This paper presents a study on a memristive two-neuron-based Hopfield neural network with fractional-order derivatives. The equilibrium points of the system are identified, and their stability is analyzed. Bifurcation diagrams are obtained by varying the magnetic induction strength and the fractional-order derivative, revealing significant changes in the system dynamics. It is observed that lower fractional orders result in an extended bistability region. Also, chaos is only observed for larger magnetic strengths and fractional orders. Additionally, the application of the fractional-order model for image encryption is explored. The results demonstrate that the encryption based on the fractional model is efficient with high key sensitivity. It leads to an encrypted image with high entropy, neglectable correlation coefficient, and uniform distribution. Furthermore, the encryption system shows resistance to differential attacks, cropping attacks, and noise pollution. The Peak Signal-to-Noise Ratio (PSNR) calculations indicate that using a fractional derivative yields a higher PSNR compared to an integer derivative. Full article

Brown algae and fish waste contain high-value compounds with potentially beneficial effects on plant growth. Several commercial fertilizer products are currently available, but the characteristics of the materials are usually not well-described. Fish and seaweed residues originating from the Norwegian coast are available, after industrial processing, which may be combined into complete fertilizers exerting additional effects on crop plants (biostimulants). In this study, raw samples of fish and seaweed residues were investigated using ecofriendly technologies (drying, leaching), targeting search and isolation of potential biostimulants, followed by physicochemical characterization (elemental analysis, UV–visible, FT-IR, ICP-MS, ICP-OES, electrical conductivity, pH, etc.). Organic solvent extractions were employed to determine the available mineral content, micro- and macro-nutrients, antioxidant compounds, and amino acid content by chemical hydrolysis. The in vitro biotoxicity profile (cell viability, morphology, migration) of the generated extracts was also perused, employing Gram-positive (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli) along with sensitive neuronal eukaryotic cell lines N2a58 and SH-SY5Y, to assess their time- and concentration-dependent efficacy as antimicrobials and agents counteracting oxidative stress. The analytical composition of all raw materials showed that they contain important nutrients (K, P, Ca, N) as well as organic compounds and amino acids (Gly, Asp, Glu, Leu, Phe) capable of acting as plant biostimulants. Concurrently, the inherently high conductivity values and salt content necessitated leaching processes, which result in Na⁺ and K⁺ decreasing by more than ~60% and justifying further their use in soil treatment formulations. The aforementioned results and assertions, combined with physical measurements (pH, electrical conductivity, etc.) on naturally occurring and dried samples as well as green solvent extracts, formulated a physicochemical profile reflecting well-defined inorganic–organic species that might function as biostimulants. The collective physicochemical and biological properties support the notion that appropriate mixtures of marine organism residues may be efficient fertilizers for crop plants and concurrently possess biostimulant characteristics. Full article

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method. Full article

Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network’s ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network’s feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method’s strong performance and feasibility for rice disease classification in real-life scenarios. Full article

Most of the world’s crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent works have used fixed hyperparameters to fine-tune the pre-trained models with the new dataset and/or applied data augmentation, such as image patching, to improve classifier performance. However, a detailed evaluation of the impact of architecture (e.g., backbone) and training (e.g., optimizer and learning rate) hyperparameters on the performance of coffee rust classification models has not been performed. Therefore, this paper presents a comprehensive study of the impact of five types of hyperparameters on the performance of coffee rust classification models. Specifically, eight pre-trained models are compared, each with four different amounts of transferred layers and three different numbers of neurons in the fully-connected (FC) layer, and the models are fine-tuned with three types of optimizers, each with three learning rate values. Comparing more than 800 models in terms of F1-score and accuracy, it is identified that the type of backbone is the hyperparameter with the greatest impact (with differences between models of up to 70%), followed by the optimizer (with differences of up to 20%). At the end of the study, specific recommendations are made on the values of the most suitable hyperparameters for the identification of this type of disease in coffee crops. Full article

The detection and identification of plant diseases is a fundamental task for sustainable crop production. Septoria tritici and Stagonospora nodorum blotch (STB and SNB) are two of the most common diseases of cereal crops that cause significant economic damage. Both pathogens are difficult to identify at early stages of infection. Determining the degree of the disease at a late infection stage is useful for assessing cereal crops before harvesting, as it allows the assessment of potential yield losses. Hyperspectral sensing could allow for automatic recognition of Septoria harmfulness on wheat in field conditions. In this research, we aimed to collect information on the hyperspectral data on wheat plants with different lesion degrees of STB&SNB and to create and train a neural network for the detection of lesions on leaves and ears caused by STB&SNB infection at the late stage of disease development. Spring wheat was artificially infected twice with Septoria pathogens in the stem elongation stage and in the heading stage. Hyperspectral reflections and brightness measurements were collected in the field on wheat leaves and ears on the 37th day after STB and the 30th day after SNB pathogen inoculation using an Ocean Insight “Flame” VIS-NIR hyperspectrometer. Obtained non-imaging data were pre-treated, and the perceptron model neural network (PNN) was created and trained based on a pairwise comparison of datasets for healthy and diseased plants. Both statistical and neural network approaches showed the high quality of the differentiation between healthy and damaged wheat plants by the hyperspectral signature. A comparison of the results of visual recognition and automatic STB&SNB estimation showed that the neural network was equally effective in the quality of the disease definition. The PNN, based on a neuron model of hyperspectral signature with a spectral step of 6 nm and 2000–4000 value datasets, showed a high quality of detection of the STB&SNB severity. There were 0.99 accuracy, 0.94 precision, 0.89 recall and 0.91 F-score metrics of the PNN model after 10,000 learning epochs. The estimation accuracy of diseased/healthy leaves ranged from 88.1 to 97.7% for different datasets. The accuracy of detection of a light and medium degree of disease was lower (38–66%). This method of non-imaging hyperspectral signature classification could be useful for the identification of the STB and SNB lesion degree identification in field conditions for pre-harvesting crop estimation. Full article

The classification of sugarcane varieties using products derived from remote sensing allows for the monitoring of plants with different profiles without necessarily having physical contact with the study objects. However, differentiating between varieties can be challenging due to the similarity of the spectral characteristics of each crop. Thus, this study aimed to classify four sugarcane varieties through deep neural networks, subsequently comparing the results with traditional machine learning techniques. In order to provide more data as input for the classification models, along with the multi-band values of the pixels and vegetation indices, other information can be obtained from the sensor bands through RGB combinations by reconciling different bands so as to yield the characteristics of crop varieties. The methodology created to discriminate sugarcane varieties consisted of a dense neural network, with the number of hidden layers determined by the greedy layer-wise method and multiples of four neurons in each layer; additionally, a 5-fold evaluation in the training data was composed of Sentinel-2 band data, vegetation indices, and RGB combinations. Comparing the results acquired from each model with the hyperparameters selected by Bayesian optimisation, except for the neural network with manually defined parameters, it was possible to observe a greater precision of 99.55% in the SVM model, followed by the neural network developed by the study, random forests, and kNN. However, the final neural network model prediction resulted in the 99.48% accuracy of a six-hidden-layers network, demonstrating the potential of using neural networks in classification. Among the characteristics that contributed the most to the classification, the chlorophyll-sensitive bands, especially B6, B7, B11, and some RGB combinations, had the most impact on the correct classification of samples by the neural network model. Thus, the regions encompassing the near-infrared and shortwave infrared regions proved to be suitable for the discrimination of sugarcane varieties. Full article

Ochratoxin A (OTA) is a common secondary metabolite of Aspergillus ochraceus, A. carbonarius, and Penicillium verrucosum. This mycotoxin is largely present as a contaminant in several cereal crops and human foodstuffs, including grapes, corn, nuts, and figs, among others. Preclinical studies have reported the involvement of OTA in metabolic, physiologic, and immunologic disturbances as well as in carcinogenesis. More recently, it has also been suggested that OTA may impair hippocampal neurogenesis in vivo and that this might be associated with learning and memory deficits. Furthermore, aside from its widely proven toxicity in tissues other than the brain, there is reason to believe that OTA contributes to neurodegenerative disorders. Thus, in this present in vivo study, we investigated this possibility by intraperitoneally (i.p.) administering 3.5 mg OTA/kg body weight to adult male mice to assess whether chronic exposure to this mycotoxin negatively affects cell viability in the dentate gyrus of the hippocampus. Immunohistochemistry assays showed that doses of 3.5 mg/kg caused a significant and dose-dependent reduction in repetitive cell division and branching (from 12% to 62%). Moreover, the number of countable astrocytes (p < 0.001), young neurons (p < 0.001), and mature neurons (p < 0.001) negatively correlated with the number of i.p. OTA injections administered (one, two, three, or six repeated doses). Our results show that OTA induced adverse effects in the hippocampus cells of adult mice brain tissue when administered in cumulative doses. Full article

Field-scale prediction methods that use remote sensing are significant in many global projects; however, the existing methods have several limitations. In particular, the characteristics of smallholder systems pose a unique challenge in the development of reliable prediction methods. Therefore, in this study, a fast and reproducible new approach to wheat prediction is developed by combining predictors derived from optical (Sentinel-2) and radar (Sentinel-1) sensors using a diverse set of machine learning and deep learning methods under a small dataset domain. This study takes place in the wheat belt region of Ethiopia and evaluates forty-two predictors that represent the major vegetation index categories of green, water, chlorophyll, dry biomass, and VH polarization SAR indices. The study also applies field-collected agronomic data from 165 farm fields for training and validation. According to results, compared to other methods, a combined automated machine learning (AutoML) approach with a generalized linear model (GLM) showed higher performance. AutoML, which reduces training time, delivered ten influential parameters. For the combined approach, the mean RMSE of wheat yield was from 0.84 to 0.98 ton/ha using ten predictors from the test dataset, achieving a 99% confidence interval. It also showed a correlation coefficient as high as 0.69 between the estimated yield and measured yield, and it was less sensitive to the small datasets used for model training and validation. A deep neural network with three hidden layers using the ten influential parameters was the second model. For this model, the mean RMSE of wheat yield was between 1.31 and 1.36 ton/ha on the test dataset, achieving a 99% confidence interval. This model used 55 neurons with respective values of 0.1, 0.5, and 1 × 10⁻⁴ for the hidden dropout ratio, input dropout ratio, and l2 regularization. The approaches implemented in this study are fast and reproducible and beneficial to predict yield at scale. These approaches could be adapted to predict grain yields of other cereal crops grown under smallholder systems in similar global production systems. Full article

Brassicaceae are natural sources of bioactive compounds able to promote gut health. Belonging to this plant family, Camelina sativa is an ancient oil crop rich in glucosinolates, polyunsaturated fatty acids, and antioxidants that is attracting renewed attention for its nutraceutical potential. This work aimed at investigating the therapeutic effects of a defatted seed meal (DSM) of Camelina sativa on the colon damage and the persistent visceral hypersensitivity associated with colitis in rats. Inflammation was induced by the intrarectal injection of 2,4-dinitrobenzenesulfonic acid (DNBS). The acute administration of Camelina sativa DSM (0.1–1 g kg⁻¹) showed a dose-dependent pain-relieving effect in DNBS-treated rats. The efficacy of the meal was slightly enhanced after bioactivation with myrosinase, which increased isothiocyanate availability, and drastically decreased by pre-treating the animals with the selective peroxisome proliferator-activated receptor alpha (PPAR α) receptor antagonist GW6471. Repeated treatments with Camelina sativa DSM (1 g kg⁻¹) meal counteracted the development, as well as the persistence, of visceral hyperalgesia in DNBS-treated animals by reducing the intestinal inflammatory damage and preventing enteric neuron damage. In conclusion, Camelina sativa meal might be employed as a nutraceutical tool to manage persistent abdominal pain in patients and to promote gut healing. Full article