Crop Yield Quality Research Articles

Recycled lithium battery nanomaterials as a sustainable nanofertilizer: Reduced peanut allergenicity and improved seed quality

The rapidly increasing amount of end-of-life lithium iron phosphate (LiFePO4) batteries has raised significant environmental concerns. This study offers a strategy for a paradigm shift by transforming this growing waste into a valuable resource by recycling discarded LiFePO4 batteries and safely integrating the materials into sustainable agriculture. We used five types of LiFePO4 (10, 50 mg kg−1) applied to soil planted with peanuts in a full-culture experiment. Our results show that addition of <50 mg kg−1 of recycled nano-LiFePO4 (rn-LiFePO4) has a multifaceted positive impact on peanut because of sustainable release of nutrients and nano-specific effects, not only enhancing photosynthesis and root growth but also increasing yield by 22 %–34 % while simultaneously elevating seed nutritional quality. Moreover, a remarkable reduction (up to 99.78 % at 10 mg kg−1 rn-LiFePO4) in the expression of allergen genes was evident following exposure to LiFePO4, which showed a significant negative correlation with Fe content in the seeds. The decreased peanut allergen gene expression was mediated by a downregulation of metabolites associated with protein digestion and absorption. Furthermore, rhizosphere soil immune system enhancement may indirectly enhance immune responses to peanut allergy. This study suggests the significant potential of nanoscale LiFePO4 recycled from Li battery, including enhancing crop yield quality and mitigating peanut allergy concerns while simultaneously addressing a growing waste stream of concern.

Computational analysis of 16D10 effector peptides from root-knot nematodes and their interaction with Mi resistance (R) protein through molecular docking

Plant-parasitic nematodes, especially root-knot nematodes (RKNs), pose significant threats to global agriculture, affecting both the quantity and quality of crop yields. Central to the parasitism strategy of RKNs is the deployment of effector proteins, such as the 16D10 peptide, which interact with host plant resistance mechanisms. This study focuses on the computational analysis and molecular docking of 16D10 effector peptides derived from major RKN species (M. incognita, M. arenaria, M. hapla, M. javanica, and M. chitwoodi) to explore their interaction with the Mi resistance protein in tomatoes. Initial bioinformatics assessments included sequence retrieval, multiple sequence alignment, and phylogenetic analysis, utilizing tools such as CLUSTALW and the MEME Suite. Notably, our phylogenetic analysis revealed a close evolutionary relationship between M. incognita and M. chitwoodi, contrasting with other RKN species based on both DNA and protein data. Subsequent, in silico 3D modeling and docking studies, conducted using Chem-Sketch and MOE software, provided insights into the structural basis of the interaction between 16D10 peptides and the Mi protein. Our findings indicate that these interactions could play a crucial role in the modulation of plant defense mechanisms, potentially leading to effector-triggered susceptibility (ETS). This study not only enhances our understanding of nematode-host interactions but also aids in the development of novel strategies for managing RKN infections in economically important crops.

Phyto-parasitic nematodes of bell pepper plant and farm soil in Abua, Rivers State, Nigeria

Bell pepper farming significantly contributes to economic development in Abua. However, phyto-parasitic nematodes are endemic in the area and affect quality of crop yield. Hence, this study was undertaken to investigate the dynamics of occurrence of nematodes in bell pepper roots and soil samples of the farms in Abua. Roots and soil from the root rhizosphere were simultaneously collected at 0-20 cm depth using an improvised soil auger and knife. Modified sieve plate technique was employed for nematode detection and a pictorial key was used for nematode identification at the genera level. An overall nematode abundance of 746 nematodes in the soil around the root region and 216 nematodes in the root tissues of the bell peppers were recorded. Gracilachus species (10.6 %) was more prevalent in the soil, and Meloidogyne species (35.2 %) exhibited the highest population in roots. The study found that Abua's cultivated soil is susceptible to nematode infestation, limiting crop performance. The nematodes showcased root burrowing which is an inherent pattern for survival. The result on root nematodes across the five bell farms surveyed was not significant (p &gt; 0.05). The result opined that farmers in Abua, Rivers State, should understand the role of the soil nematodes and implement soil improvement strategies for improved crop performance and rural economic development.

Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture.

Impact of “Dry Sowing and Wet Emergence” Water Regulation on Physiological Growth Characteristics and Water Productivity of Cotton Fields in Southern Xinjiang Province

Determining a suitable “dry sowing and wet emergence” water control program for cotton fields in the arid regions of Northwest China is of great significance in saving water resources, improving economic efficiency, and promoting sustainable development of agriculture. The objective of this study was to analyze the effects of different “dry sowing and wet emergence” water control treatments on dry matter accumulation, chlorophyll fluorescence, yield quality, and water productivity of cotton, and to determine the optimal “dry sowing and wet emergence” water control program for cotton growth in arid areas. A two-year experiment was carried out in 2021 and 2022 in mulched drip-irrigated cotton fields, with a total of 13 treatments of different seedling water quantities (2021: 67.5 mm, 90 mm, 112.5 mm; 2022: 6 mm, 10.5 mm, 15 mm) and different drip frequencies (frequencies means number of drops at seedling stage) (2021: one, two, three times; 2022: two times, four times) in the “dry sowing and wet emergence”. Results indicated a positive correlation between increased seedling water quantity and growth indexes. High seedling water quantity treatment demonstrated a 14.33% higher cotton yield than the low seedling water quantity treatment. In comparison with low-frequency treatment, the high-frequency treatment exhibited significantly larger cotton plant height, dry matter accumulation, and yield. Over two years, the average values increased by 8.69%, 16.4%, and 15.91%, respectively, with a 14.55% increase in the coefficient of photochemical quenching of the leaf blade (qP). The high frequency and larger amount of seedling water quantity treatments showed significantly higher irrigation water productivity, with increases of 39.2% and 70.2% compared to the winter irrigation control treatment. In summary, the appropriate “dry sowing wet emergence” water regulation mode (the first drip: 15 mm, the second drip: 4.5 mm, the third drip: 22.5 mm, the fourth drip: 15 mm) can ensure crop yield quality under the premise of significantly reducing the agricultural irrigation water, which can provide certain theoretical support for the green, efficient, and sustainable development of the local cotton industry.

Floral interventions enhance flower visitor communities and pollination services in moringa plantations

Abstract Pollination is a crucial ecosystem service contributing to global food security. Moringa (Moringa oleifera) is an economically important tropical crop, mostly cultivated by smallholder farmers. Well‐established approaches of planting flower‐rich patches used in temperate agroecosystems for pollination enhancement were adapted to tropical moringa systems. Based on existing evidence, we hypothesised that floral interventions would improve flower visitor diversity and abundance in the crop, thereby increasing pollination services and moringa yield and quality. We used standardised methods to survey flower visitors on moringa and assess economically relevant measures of crop yield quality and quantity. We selected 24 moringa fields in Tamil Nadu, India, to compare fields with and without floral interventions. We planted red gram, Cajanus cajan as a border crop, and marigold, Tagetes erecta as an intercrop on moringa fields to enhance floral resource availability for pollinators. These interventions were co‐designed with local farmers to ensure additional benefits to their community. We found that flower visitor abundance and species richness were significantly (50% for abundance and 33% for species richness) greater in fields with floral interventions compared with control fields. We also found that the percentage of flowers that resulted in harvestable fruits were significantly (30%) greater in fields with floral intervention. Production benefits in yield and quality were significantly positively correlated with the abundance and species richness of flower visitors. Synthesis and applications. Our results provide clear evidence that floral interventions in the form of intercropping and border cropping can enhance pollinator communities and services they provide in tropical smallholder systems. These findings underpin a practical management option for farmers to enhance flower visitor communities and pollination services, which can potentially also provide additional co‐benefits to farmers, improving livelihoods and sustainable production.

How Does Agricultural Water Resources Management Adapt to Climate Change? A Summary Approach

This editorial paper takes the form of a concise report and delves into a critical and intricate issue essential for the sustainability of agriculture. It centers on the intricate relationship between agri-cultural water resource management and agronomical practices, as well as their ability to adapt to the impacts of climate change while ensuring both the quantity and quality of crop yields. Specifically, this paper serves as a synopsis of how the far-reaching consequences of climate change for water resources impact agricultural production. It also highlights primary adaptation strategies for managing agricultural water resources, as drawn from the existing literature. Such strategies are designed to counteract the potentially adverse impacts of climate change on the rural sector. Fur-thermore, this brief report offers a valuable overview of the 17 selected papers featured in this Special Issue (SI) on Water, published by MDPI. These papers serve as exemplars of cutting-edge approaches to adaptability in water resource management and resilient crop production systems, as these fields attempt to thrive in an ever-changing environmental landscape.

Deep learning pest detection on Indonesian red chili pepper plant based on fine-tuned YOLOv5

.This research developed a pest detection model for Indonesian red chili pepper based on fine-tuned YOLOv5. Indonesian red chili pepper is the third largest vegetable commodity produced in Indonesia. Pest attacks disrupt the quantity and quality of crop yields. To control pests effectively, it is necessary to detect the type of pest correctly. A viable solution is to leverage computer vision and deep learning technologies. However, no previous studies have developed a pest detection model for Indonesian red chili pepper based on this technology. YOLOv5 is a variant of the YOLO object detection algorithm, which has major advantages in terms of computation cost and execution speed. The dataset comprises 4,994 image files collected from a chili plantation in Bengkulu province, Indonesia, covering 4 different classes and a total of 10,683 pests. The image is 1216 x1216 px with the smallest, largest, and average object dimensions of 2%, 35%, and 4% of the image dimensions. The training model used is fine-tuning YOLOv5s with variations of patience as an early stop parameter of 100, 200, and 300. The evaluation of the trained model is based on train loss, validation loss, and mAP@0.5:0.95, the best-trained model is the 445th epoch on patience 100 with the best confidence value of 0.321 and the highest TF1 of 0.74. From the best-trained model testing on the test dataset, the mAP@0.5 performance for all classes is 81.3%. The model not only detected large pests but was also able to detect objects that were small in size compared to the image size. The best-trained model's best mAP@0.5 performance and speed are 82.6% and 20 ms/image, or 50 fps on NVIDIA P100 GPU.

Design and Analysis of Proximal Soil Sensing System Based on Electrochemical ISE Sensor for Total Nitrogen and Potassium Fertility Assessment

Traditional agriculture is the most practiced form of agriculture around the world. Conventional agriculture depends on applying harmful chemicals to the soil and plants, which are mostly synthetically formulated in a laboratory. A modern approach is known as Precision Agriculture (PA) has been introduced in the last two decades produce healthier crops and higher yields compared to conventional agriculture. By using precision agriculture, the farmers can improve their farming techniques and solve decades-old problems. From the study, plants suffering from a lack of N and K show decreasing photosynthetic activity and plants’ abilities to resist disease, leading to reduced crop yield quality and overall quantity. To solved this, the assessment and data analysis for N and K is required. This study aims to get the accurate value of N and K fertilizer used in the field to help restore the ecosystem and optimize yield while remaining environmentally sustainable. The fertilizer A and B will fill and mix into the aqueous solution, then proximal soil sensing ISE sensor will measure the concentration of Nitrate and Potassium ions in aqueous samples. The feedback signal from the ISE sensor will enter the Raspberry Pi controller and analysed through the Internet of Things (IOT) platform using the Node Red and Phyton simulation. Lastly, the data will be processed and monitored through the LCD. The evaluation performance and the result of the ISE Sensor will be validated using a correlation method. The correlation of the soil sensor and the lab analysis has shown that the right amount of NK fertilizer can help the farmer to manage the plant in the field.

Microalgae as Biofertilizers: A Sustainable Way to Improve Soil Fertility and Plant Growth

The intensification of agricultural production in response to the global population increase and the growing demand for food has raised significant concerns regarding environmental impacts over the past few decades. Currently, modern agriculture aims to improve the quantity and quality of crop yield, minimizing the negative effects of treatments on the environment. Recently, microalgae have found extensive application as a valuable biological resource across multiple industries, including the food sector, biofuel production, and the pharmaceutical industry. In agriculture, microalgae have been seen as a promising and sustainable alternative to agrochemicals, offering a range of benefits to improve soil fertility, optimize nutrient management, and reduce reliance on synthetic fertilizers. In general, microalgae have demonstrated efficient nutrient cycling abilities, assimilating and converting essential nutrients, such as nitrogen, phosphorus, and potassium, into forms readily available for plants. Additionally, they produce bioactive substances, including phytohormones, which have a direct impact on the physiological processes of plants and promote their growth. Microalgae can also establish beneficial interactions with other soil microorganisms, supporting the growth of beneficial bacteria and fungi, thus promoting a healthy soil microbiome. On the other hand, as photosynthetic microorganisms, microalgae harness sunlight to convert carbon dioxide (CO2) into organic matter through photosynthesis. This ability allows them to sequester carbon and contribute to sustainable agriculture by reducing greenhouse gas emissions. The present work provides an overview of the potential of microalgae as biofertilizers, highlighting their unique characteristics, benefits, and main limitations for effective implementation in agriculturally sustainable practices.

Implementation of Rainfall Forecasting using ANN

Prediction of rainfall is essential for many sectors, including flood mitigation, disaster prevention, agriculture production, and water resource management. Rain forecasts are used to warn of natural disasters such as floods or to plan planting activities to improve the quality of crop yields. An accurate rainfall prediction remains a challenging task due to the uncertainty of natural phenomena considering that rainfall is a non-linear and dynamic process. Various climatic variables such as temperature, relative humidity, wind speed and direction, are among those that affect the dynamic process of rainfall. This study suggests a machine learning technique based on Artificial Neural Network (ANN) model to forecast rainfall by using two-years historical local rain rate and others meteorological data. Evaluation metrics such as mean absolute error, root mean square error, and correlation coefficient, are used to evaluate model’s performance. The results show good performance and sensible prediction accuracy. The comparison with existing model clearly depicted that ANN model predicts rainfall better than statistical models particularly at lower and moderate rain rate.

Phytochemical Screening, Bioactive Compound Quantification by HPLC, and Antifungal Properties of Andrographis paniculata Extracts Against Plant Pathogenic Fungi

The common issues that occurred in plant disease had been studied for many years and fungi were determined as the major microbial agent that typically reduces the crop yield quality and economic losses. The increasing of antimicrobial resistance fungi and chemical fungicides has urged the development of new antifungal agents from medicinal plants such as Andrographis paniculata which is riches in phytochemical compounds. Hence, this research was carried out to investigate the phytochemical compounds and antifungal properties of a botanical extract derived from A. paniculata to inhibit fungal growth. A maceration technique was used where the extracts were soaked in methanol solvent at room temperature (±28°C) for 72 hours. The prepared samples were studied for phytochemical screening, and it shows the presence of alkaloids, flavonoids, saponins, tannins, and terpenoids. Whereas the quantitative estimation of andrographolide compound was conducted by reversed-phase high performance liquid chromatography (HPLC) method. The antifungal test was tested on Fusarium spp., Colletotrichum spp., Neoscytalidium dimidiatum, and Thielaviopsis paradoxa through agar disc diffusion. The result revealed that it exhibits significant fungal growth inhibition. Therefore, this study presented the versatile use of A. paniculata extract in developing its role as a novel broad-spectrum antifungal agent against plant disease.

Significance of Soil Siderophore-Producing Bacteria in Evaluation and Elevation of Crop Yield

Iron is a vital element for plant and microbial growth; yet, the major portion of iron in soils is in the form of (oxi-)hydroxides with limited bioavailability, resulting in decreased crop yield quality. In response to iron deficiency, soil microorganisms produce siderophores that transform insoluble iron into a soluble form that plants and microorganisms can use. The abundance and activity of siderophore-producing bacteria (SPB) might be used as a biological assessment index for the fertility status of cultivated land. In order to achieve this goal, it is critical to investigate the influences of SPB on plant growth and soil quality. In this study, we performed a mixed-effect model meta-analysis on 342 research studies that compared plant growth with and without SPB. The findings revealed that SPB increased plant growth significantly (up to 30%). The stimulating effects on plants followed the sequences of pant weight, plant height, and germination rate.

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics.

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.

Effectiveness Of Bioherbicide Kiambang (Salvinia molesta) on Growth of Gogo Rice (Oryza sativa L.)

The main problem of upland rice cultivation is the presence of weeds, which cause a decrease in the quantity and quality of crop yields. Generally, farmers use synthetic herbicides to control weeds. One way to reduce the use of synthetic herbicides is by using kiambang extract bioherbicides which are applied to weeds. Currently, there is no research to determine the effect of kiambang extract on upland rice germination. The purpose of this study was to assess the effect of kiambang bioherbicide on the germination phase of upland rice by giving several concentrations of kiambang bioherbicide. This study used a completely randomized design (CRD) with 5 concentration treatments, namely 2,4-D herbicide, 20%, 40%, 60%, and 80% bioherbicide. Kiambang extract concentration was repeated 3 applicates for each replication; each experimental unit consisted of three Petri dishes containing 25 rice seeds. The research parameters were germination percentage, germination rate, length of the plumule, and radicle. Observational data were analyzed using analysis of variance (F test) at a level of 5% and continued with the Least Significance Different (LSD) test at a level of 5% to determine the difference between treatments. The results showed that the bioherbicide extract of kiambang (Salvinia molesta) 20% did not inhibit seed germination, but the seeds grew abnormally, while concentrations of 40%, 60%, and 80% inhibited germination.

A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation, and a capsule neural network (CapsNet) is used in this paper to tackle these challenges. The proposed method involves three phases. First, a graph-based technique extracts leaf area from a plant image. The second step expands the dataset using an Efficient Generative Adversarial Network E-GAN. Third, a CapsNet identifies the illness and stage. The proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf datasets. The proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.

Influence of foliar application with Moringa oleifera residue fertilizer on growth, and yield quality of leafy vegetables

Biofertilizers produced from organic materials help to promote the growth, and yield quality of crops and is more environmentally friendly than chemical fertilizers. Moringa oleifera is a leafy vegetable whose leaves are also used to make biofertilizers. The use of moringa non-edible parts in biofertilizer preparation remains under-explored. In this study, a procedure to produce moringa foliar biofertilizer (MFB) from non-edible parts was developed. The effect of composting time (3 to 4 months) on the quality of MFB was investigated, and four-month incubation was found suitable for biofertilizers yield with the highest nitrogen content and optimal pH. Furthermore, the influences of MFB doses (20 to 100 mL per Litre) on the growth of lettuce and mustard spinach were studied. The yield of these leafy vegetables was the highest at 100 mL per Litre of MFB spray. Finally, MFB was compared with other commercial foliar sprays, including chitosan fertilizer and seaweed fertilizer. Each foliar treatment was applied every five days until five days before harvest. Plant height, the number of leaves, canopy diameter, leaf area index, actual yield, ascorbic acid content, and Brix were found to be similar in lettuce sprayed with MFB, chitosan, and seaweed fertilizers. In conclusion, the application of MFB promoted the growth and yield of mustard spinach.

Impact of Organic and Chemical Nitrogen Fertilizers on the Crop Yield and Fertilizer Use Efficiency of Soybean–Maize Intercropping Systems

The effect of the mixture (1:1) of chemical and organic nitrogen (N) fertilizer on crop yield quality and N fertilizer use efficiency remains elusive. A nitrogen field experiment was conducted in the growing seasons of 2020 and 2021 to investigate the effects of the mixture of chemical and organic N fertilizer on the crop yield, crop quality and nitrogen fertilizer use efficiency in a maize–soybean intercropping system in China. Four treatments applied at 150 kg N ha−1 were used: no nitrogen fertilizer (CK), chemical N fertilizer (ChemF), mixture (1:1) of chemical and organic N fertilizer (ChemF + OrgF) and organic N fertilizer (OrgF). The results showed that the yield and aboveground N accumulation of both soybean and maize increased with the application of fertilizer. The ChemF + OrgF treatment had lower maize and soybean seed yields than for ChemF treatment, but higher than the other two treatments in both years, and the maize yield of the (ChemF + OrgF) treatment was significantly higher (14.9%) in 2021 than 2020. Yields were significantly positively correlated with aboveground N accumulation and fertilizer use efficiency, measured using the nitrogen partial productivity (NPP), nitrogen agronomic efficiency (NAE) and nitrogen fertilizer recovery rate (NFRR). The protein content tended to increase and the oil content tended to decrease under (ChemF + OrgF) applications in soybeans. The (ChemF + OrgF) treatment had the lowest starch content in maize. There was no significant difference in the nitrogen harvest index among treatments, while the NPP, NAE and NFRR were the highest for the application of chemical N fertilizer and significantly decreased with the addition of organic N fertilizer. We conclude that the mixture (1:1) of chemical and organic N fertilizer increased the seed yield and quality of maize, but only the seed yield of soybean.

Instrumentation system for data acquisition and monitoring of hydroponic farming using ESP32 via Google Firebase

This article discusses the design of a hydroponic planting process monitoring system based on the internet of things. This device uses an ESP32 microcontroller board as the main controller. The parameters that were monitored and acquired were the conditions of the hydroponic growing media. Those parameters are; water pH, water temperature, water turbidity level, and ambient air temperature and humidity. The five parameters are measured by analog sensors integrated with the ESP32. These parameters affect the growth process and the quality of crop yields. This article also describes the calibration method for each sensor used for parameter measurement. Then the monitoring of these parameters is carried out by utilizing a real-time database, namely Google Firebase. This platform is very suitable for all IoT-based monitoring and control applications. Measurement result data is uploaded and saved to the real-time database. Then paired by Android-based applications. This application was created to be used by hydroponic farmers who use this device. Thus the results of monitoring can be used to optimize the process of growing hydroponic plants.

Use of compost in the uptake mitigation of arsenic in Beta vulgaris L. var. cicla.

Arsenic (As) may represent a risk for crop yield quality and human health since it may accumulate in the edible plant organs with the potential of leading to acute or chronic toxic effects in varied segments of the population. Management of soil fertility through compost has proven to be a valuable practice for increasing and maintaining soil organic matter, with nutritional benefits for crops. This work aimed to evaluate Swiss chard yield and the change in the bioavailability, bioaccumulation, and partitioning of As in the response of the use of compost or conventional mineral fertilization in an open-field trial conducted in a volcanic area in central Italy characterized by the natural contamination of As in soil. Compost treatment led to a short-term increase trend in soil organic carbon, total nitrogen, and available phosphorus in a significant way. In the compost-amended plots, the mitigation of the As uptake was detected in leaves, which are the edible part of Swiss chard. The As bioaccumulation factor in leaves of Swiss chard and the translocation factor for leaves/roots were also decreased using compost. Fertilization by compost can improve soil fertility, sustain Swiss chard production, and mitigate As accumulation in leaves of this crop grown in a naturally As-contaminated soil. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.