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Soil is an important reservoir of innumerable natural and biological resources fundamental to the sustainability of life and the earth's functionality. The soil is complex due to changing biodiversity, physicochemical characteristics, disturbances, and pedogenesis, which are constituent indices required for the measurement of its healthiness. Hence, there is a need to concertedly protect the soil by consciously promoting practices and behaviors that optimize its priority functions in delivering ecosystem services. It is further significant for crop yield, hence the need to pay more attention to its health. Soil healthiness is also a reflection of its capacity to support biogeochemical processes, abiotic communities, and plant and animal productions. However, agronomic studies, until recently, focused more on the use of chemical indicators in determining soil health, despite the versatile ecophysiological role of microorganisms in soil formation, resource cycling, and management. These biological phenomena expressed by soil microbial communities form the basis for the conversion of diverse organic matters into bioutilizable resources for plants' healthy development. This review, therefore, explored the underlining mechanisms, particularly climate change-related, that caused divergent soil properties and how this impacted the microbial composition of healthy soil. Likewise, several pieces of agronomic literature on the physical characteristics, ecological services, and functions of a healthy soil were compared toward innovative best management practices for improving soil health. [ABSTRACT FROM AUTHOR]

Agriculture stands as the bedrock of Malawi's economy, involving nearly 90% of the population in subsistence farming. However, the sector faces challenges arising from unpredictable weather patterns, climate shifts, and environmental factors that threaten its sustainability. This paper proposes a pioneering solution leveraging Machine Learning (ML) to address these challenges, presenting a robust decision support system for Crop Yield Prediction (CYP). By harnessing ML capabilities, the system aids in crucial decisions related to crop selection and management throughout the growing season, specifically tailored for the unique agricultural landscape of Malawi. This approach aims to empower farmers by providing valuable insights into soil quality, composition, and nutrients, enabling informed decisions to maximize crop yield. Through the integration of advanced technology into the agricultural domain, this paper seeks to usher in a transformative era for Malawian agriculture, fostering resilience and sustainability in the face of evolving environmental dynamics. [ABSTRACT FROM AUTHOR]

Agriculture is a key component of Telangana's economy, and greater performance in this sector is crucial for inclusive growth. A central challenge is yielding estimation to predict crop yields before harvesting. This paper addresses this challenge with machine learning approaches includes Naive Bayes, KNN and Random Forest. The parameters considered for model testing are crop, season, rainfall and location. This paper includes a case study of Telangana with the help of Telangana weather data set to provide analysis on the key factors like overall rainfall recorded with respect to each Mandal, overall seasonal yield in selected years, seasonal yield of major crops like Bengal gram, groundnut and maize, and overall yield in two different agricultural seasons: rabi and kharif. Random forest machine learning model produces highest accuracy of 99.32% when compared with other process models. [ABSTRACT FROM AUTHOR]

Every year agribusinesses develop and market new seed varieties with traits desirable for different planting environments. When agribusinesses experiment the new varieties at different farms, data is generated about the performance of these new seed varieties. However, farmers do not have a decision support tool to process the vast amount of yield performance data to make an informed seed variety selection decision for their farm. An informed decision requires accurate estimation of yield performances of seed varieties on the targeted farmland and balancing trade-offs between the expected yield and the risk associated with the seed varieties selected to grow. This research uses a real data set provided by Syngenta—an agribusiness—to create a decision-support tool. The data set used in this research contains yield information of different soybean varieties experimented at different farms located in the Midwest of the US, as well as information on location, soil, and weather conditions prevailing in those farms. In addition to this data, we also surveyed soybean farmers to understand their preferences and current practices in choosing seed varieties to grow in their farms. We are the first to capture and document farmers' preferences and practices in selecting and growing soybean varieties. The data collected from the survey enabled us to compare the results emerging from the proposed methodology with the status quo practices. Using the Syngenta data and survey responses, this paper proposes an analytics framework that integrates machine learning, clustering, simulation, and portfolio optimization to optimally select soybean varieties to grow at the target farm. We choose a machine learning model, which simulates the yield performance of soybean varieties under different plausible weather scenarios derived from the neighborhood of the target farm. The simulated yields are then used to estimate parameters in a portfolio optimization formulation that selects the optimal portfolio of seed varieties to grow at the target farm. The main methodological contribution of this research is in the development of an approach that integrates machine learning, clustering, simulation, and portfolio optimization to help farmers make an important decision. Specifically, we introduce a novel data-driven simulation-based approach to estimate the parameters needed to solve a portfolio optimization problem. Our analysis indicates that an average farmer will gain as much as $177,369 per year in revenue by utilizing the analytics framework introduced in this research. The methodology developed in this research can be applied to variety selection decisions for other crops and influence farming practice positively. By embracing the machine learning and analytics powered framework introduced in this paper, agribusinesses can position themselves as the innovation leader and create business value by unleashing the potential of the scientific discoveries of agronomy to offer tailored farming decision support to individual farmers. [ABSTRACT FROM AUTHOR]

Precision agriculture, a transformative farming approach, has gained prominence due to advancements in digital technologies. This paper explores the multifaceted landscape of precision agriculture, focusing on its tangible benefits, challenges, and future directions. Purpose: Amidst the growing interest in precision agriculture, this paper aims to provide a comprehensive analysis of its various aspects. Specifically, it seeks to elucidate the benefits of precision agriculture in optimizing resource utilization, enhancing crop health, and promoting sustainability. Moreover, it examines the challenges faced in its implementation and proposes future directions to overcome these obstacles. Findings: Through a review of existing literature and case studies, this paper presents a nuanced understanding of precision agriculture's impact on crop farming, livestock production, economic outcomes, and environmental sustainability. It identifies key challenges such as data security, implementation costs, and regulatory frameworks, while also highlighting innovative solutions and promising advancements in the field. Originality: To the best of our knowledge, this paper represents a rigorous attempt to comprehensively analyze the landscape of precision agriculture, with a focus on its original contributions to the field. By synthesizing existing research and offering insights into future directions, it adds to the emerging knowledge base surrounding precision agriculture and its potential to revolutionize modern farming practices. [ABSTRACT FROM AUTHOR]

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields. [ABSTRACT FROM AUTHOR]

Bird invasion will reduce the yield of high-value crops, which threatens the healthy development of agricultural economy. Sonic bird repellent has the advantages of large range, no time and geographical restrictions, and low cost, which has attracted people's attention in the field of agriculture. At present, there are few studies on the application of sonic bird repellents in pear orchards to minimize economic losses and prolong the adaptive capacity of birds. In this paper, a sound wave bird repellent system based on computer vision is designed, which combines deep learning target recognition technology to accurately identify birds and drive them away. The neural network model that can recognize birds is first trained and deployed to the server. Live video is captured by an installed webcam, and the sonic bird repellent is powered by an ESP-8266 relay switch. In a pear orchard, two experimental areas were divided into two experimental areas to test the designed sonic bird repellent device, and the number of bad fruits pecked by birds was used as an indicator to evaluate the bird repelling effect. The results showed that the pear pecked fruit rate was 6.03% in the pear orchard area that used the acoustic bird repeller based on computer recognition, 7.29% in the pear orchard area of the control group that used the acoustic bird repeller with continuous operation, and 13.07% in the pear orchard area that did not use any bird repellent device. While acoustic bird repellers based on computer vision can be more effective at repelling birds, they can be used in combination with methods such as fruit bags to reduce the economic damage caused by birds. [ABSTRACT FROM AUTHOR]

Undernutrition is a confront to the health and output of the populace. It is viewed as one of the five leading contrary health impacts of climate variability and is defined as different measures of nutritional status. We aimed to assess the scientific evidence base for the impact of climate variability on childhood undernutrition (particularly wasting and underweight) in low- and middle-income countries. A systematic review was conducted to identify the peer-reviewed and gray full-text studies in English with no limits for the year of publication and study design. This review covers only published studies from four databases (PubMed, Scopus, Web of Science, and Science Direct). The risk of bias was assessed using the ROVBIS tool in individual studies. The PRISMA Statement checklist for systematic reviews was referred for this review process. A significant correlation between climate variables, temperature, rainfall, and drought, and at least one undernutrition parameter in 19 out of 22 studies was observed in this systematic review. In addition, we note that crop yield, maternal education, nutritional status of mothers, wealth status at the household level, and individual levels also play substantial roles in mediating the nutritional impacts. The findings of our analysis imply that exposure to climate variables may be linked to an increased risk of undernutrition both during and for several years following climate events. This may imply that undernutrition is never caused by temperature, precipitation, drought, or other weather-related factors alone but rather that undernutrition is triggered in children who are already at risk. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the influence of climatic characteristics on the rating of land suitability for agricultural use. Soil fertility is one of the most important factors in land productivity and crop capacity; it is a complex value that depends not only on agrophysical and agrochemical soil properties but also on other natural factors, such as climate. There are different methodical approaches for a quantitative assessment of fertility level. The objectives of the research were to understand whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use in the example of the Leningrad region. The estimation and comparison between Semenov–Blagovidov's method of quality land estimation and Karmanov's method of appraisal of soils are given in this article. Karmanov's method is highlighted in this paper for its ability to assess soil's ecological indices more effectively than traditional methods. The research suggested that climate change may lead to increased variability in soil quality, with potential benefits for agriculture under certain climate scenarios, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields. The results of such assessment show that the performed calculation models can be used to forecast crop yields for future periods. [ABSTRACT FROM AUTHOR]

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The total number of articles reviewed for crop yield prediction using ML, meta-modeling (Crop models coupled with ML/DL), and DL-based prediction models and input parameter selection is 125. We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers. Each study is assessed based on the crop type, input parameters employed for prediction, the modeling techniques adopted, and the evaluation metrics used for estimating model performance. We also discuss the ethical and social impacts of AI on agriculture. However, various approaches presented in the scientific literature have delivered impressive predictions, they are complicated due to intricate, multifactorial influences on crop growth and the need for accurate data-driven models. Therefore, thorough research is required to deal with challenges in predicting agricultural output. [ABSTRACT FROM AUTHOR]

We utilize a coupled economy–agroecology–hydrology modeling framework to capture the cascading impacts of climate change mitigation policy on agriculture and the resulting water quality cobenefits. We analyze a policy that assigns a range of United States government’s social cost of carbon estimates ($51, $76, and $152/ton of CO2-equivalents) to fossil fuel–based CO2 emissions. This policy raises energy costs and, importantly for agriculture, boosts the price of nitrogen fertilizer production. At the highest carbon price, US carbon emissions are reduced by about 50%, and nitrogen fertilizer prices rise by about 90%, leading to an approximate 15% reduction in fertilizer applications for corn production across the Mississippi River Basin. Corn and soybean production declines by about 7%, increasing crop prices by 6%, while nitrate leaching declines by about 10%. Simulated nitrate export to the Gulf of Mexico decreases by 8%, ultimately shrinking the average midsummer area of the Gulf of Mexico hypoxic area by 3% and hypoxic volume by 4%. We also consider the additional benefits of restored wetlands to mitigate nitrogen loading to reduce hypoxia in the Gulf of Mexico and find a targeted wetland restoration scenario approximately doubles the effect of a low to moderate social cost of carbon. Wetland restoration alone exhibited spillover effects that increased nitrate leaching in other parts of the basin which were mitigated with the inclusion of the carbon policy. We conclude that a national climate policy aimed at reducing greenhouse gas emissions in the United States would have important water quality cobenefits. [ABSTRACT FROM AUTHOR]

The Pythagorean fuzzy set and interval-valued intuitionistic fuzzy set are the basis of the interval-valued Pythagorean fuzzy set (IVPFS) which offers an effective approach to addressing the complex uncertainty in decision-analysis processes, making it applicable across a broad spectrum of applications. This paper introduces several aggregation operators within the IVPF framework, such as the interval-valued Pythagorean fuzzy SS power weighted average operator, and the interval-valued Pythagorean fuzzy SS power geometric operator using the notion of power aggregation operators through Schweizer and Sklar (SS) operations. The existence of SS t-norms and t-conorms in the IVPF framework for addressing multi-attribute decision-making problems gives the generated operator's ability to make the information aggregation approach more adaptable compared to other current ones. The application of the proposed approach holds the potential to enhance crop yield, optimize resource utilization, and contribute to the overall sustainability of agriculture. Additionally, sensitivity and comparative analyses are provided to clarify the stability and dependability of the results acquired through this approach. [ABSTRACT FROM AUTHOR]

Crop yield estimation is a pivotal matter in agricultural management, specifically under the backdrop of demographic growth and changing climatic conditions. Many studies have been conducted employing remote sensing for crop yield estimation. However, most were specifically concentrated on condition-based environmental monitoring systems. A shortage of exclusive applications persists regarding the use of remote sensing for soil health monitoring and implementing necessary measures to enhance crop yield. To address such insufficiency, the Linear Z-score and Gaussian Radial Artificial Neural Network-based (LZ- GRANN) crop yield estimation method is proposed in this paper to enhance productivity. The performance evaluation of the proposed LZ-GRANN method reduced the overall crop yield estimation time and error by 59% and 58% and improved precision and accuracy by 23% and 26% in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. [ABSTRACT FROM AUTHOR]

In recent years, crop yield prediction has become a research hotspot in the field of agricultural science, playing a decisive role in the economic development of every country. Therefore, accurate and timely prediction of crop yields is of great significance for the national formulation of relevant economic policies and provides a reasonable basis for agricultural decision-making. The results obtained through prediction can selectively observe the impact of factors such as crop growth cycles, soil changes, and rainfall distribution on crop yields, which is crucial for predicting crop yields. Although traditional machine learning methods can obtain an estimated crop yield value and to some extent reflect the current growth status of crops, their prediction accuracy is relatively low, with significant deviations from actual yields, and they fail to achieve satisfactory results. To address these issues, after in-depth research on the development and current status of crop yield prediction, and a comparative analysis of the advantages and problems of domestic and foreign yield prediction algorithms, this paper summarizes the methods of crop yield prediction based on deep learning. This includes analyzing and summarizing existing major prediction models, analyzing prediction methods for different crops, and finally providing relevant views and suggestions on the future development direction of applying deep learning to crop yield prediction research. [ABSTRACT FROM AUTHOR]

Real-time seed detection on resource-constrained embedded devices is essential for the agriculture industry and crop yield. However, traditional seed variety detection methods either suffer from low accuracy or cannot directly run on embedded devices with desirable real-time performance. In this paper, we focus on the detection of rapeseed varieties and design a dual-dimensional (spatial and channel) pruning method to lighten the YOLOv7 (a popular object detection model based on deep learning). We design experiments to prove the effectiveness of the spatial dimension pruning strategy. And after evaluating three different channel pruning methods, we select the custom ratio layer-by-layer pruning, which offers the best performance for the model. The results show that using custom ratio layer-by-layer pruning can achieve the best model performance. Compared to the YOLOv7 model, this approach results in mAP increasing from 96.68% to 96.89%, the number of parameters reducing from 36.5 M to 9.19 M, and the inference time per image on the Raspberry Pi 4B reducing from 4.48 s to 1.18 s. Overall, our model is suitable for deployment on embedded devices and can perform real-time detection tasks accurately and efficiently in various application scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces a "Smart Fertilizing Using Internet of Things (IoT) Multi-Sensors" system to enhance fertilizer management in agriculture. The system has four main parts: the Nutro Determining Unit (NDU), the Nutro Sensing Unit (NSU), the Nutro UAV Variable Fertiliser Spray System, and a Variable Rate Unmanned Aerial Vehicle (UAV) Sprayer model. The NDU collects vital data on Soil Moisture (SM) and Environmental Conditions (EnC) using advanced IoT cameras, while the NSU consolidates and normalises the data for advanced analysis using Heuristic Decision Trees (HDT) and Random Forest (RF) algorithms. In India, a data-driven UAV system uses IoT and UAV technologies to determine nutrient needs and create a prescription map for fertilizer application. The approach caused increases in the efficient utilisation of resources, Crop Yield (CY), and ecological footprint when it underwent evaluation in a crop maize field that was 14 hectares in size. A fresh benchmark for Smart Farming (SF) techniques has been set up by this method of operation, which is motivated by data and symbolises an important innovation in modern and ecologically conscious SF methods. [ABSTRACT FROM AUTHOR]

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Due to the pressure on South Africa's irrigated agriculture to improve efficiency and optimal water use, irrigators must consider alternative water sources, such as root-accessible shallow groundwater tables, to supply the crop evapotranspiration requirement. Devising irrigation scheduling strategies that will optimize conjunctive water use is difficult because the contribution of shallow groundwater tables is not directly observed and is a function of irrigation management decisions; as a result, very few irrigators use these strategies. This paper aims to evaluate the profitability of using shallow groundwater tables as a source of irrigation water to satisfy crop evapotranspiration requirements. A bio-economic simulation model consisting of the soil–water–atmosphere–plant model and an economic accounting module was developed to calculate the profitability of conjunctive irrigation practices under different states of nature. The bio-economic simulation model was linked to a differential evolutionary algorithm to optimize the irrigation scheduling decisions. The results showed that irrigators could substantially increase profitability and water use efficiency if they consider the shallow groundwater table in their irrigation decision. About 51% of crop evapotranspiration could originate from shallow groundwater tables, reducing the irrigation requirements substantially without impacting crop yields. Sequential adaptive irrigation decision-making does not improve the bio-economic indicators much since using the shallow groundwater table mitigates the risk of undersupplying water. Therefore, conjunctive water use strategies using shallow groundwater tables economically benefit irrigators. However, a complex interplay exists between irrigation adjustments, crop yields and economic performance in different states, emphasizing the careful consideration of context-specific factors in irrigation management decisions. [ABSTRACT FROM AUTHOR]

Deep vertical rotary tillage (DVRT) is an innovative soil tillage technology that has been widely adopted in China and shown significant potential in enhancing soil quality, optimizing water use efficiency, and increasing crop yields across diverse ecological and agronomic conditions. DVRT utilizes a vertical spiral drill bit for deep plowing, which preserves soil structure, reduces soil compaction, and improves water retention, making it particularly effective in regions facing climatic challenges such as drought. This review synthesizes the effects of DVRT on soil's physical and chemical properties, crop root systems, photosynthesis, and water use efficiency, demonstrating its advantages in promoting robust root development and improving nutrient utilization. Although the technology has been applied successfully across various crops and regions, its nationwide adoption remains limited. This paper emphasizes the need for further research to refine the theoretical framework of DVRT and develop tailored strategies for different local conditions. Additionally, integrating DVRT with other agronomic practices and advancing machinery design, supported by policy measures, is essential for maximizing its benefits. In conclusion, DVRT presents a promising approach for sustainable agriculture in China, contributing to improved soil quality, increased crop yields, and enhanced food security. [ABSTRACT FROM AUTHOR]

A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) has brought about transformative changes across diverse industries, and its application in agriculture, particularly in soil management, has become a focal point of research and innovation. This paper explores the paradigm shift enabled by IoT in detecting organic carbon in soil, with the overarching goal of advancing agricultural productivity and sustainability. Emphasizing the pivotal role of organic carbon in soil health, the research addresses the existing challenges associated with its detection and underscores the potential advantages offered by IoT-based solutions for precise and real-time monitoring. The study meticulously examines various IoT technologies, including sensors, data analytics, and wireless communication protocols, within the specific context of organic carbon detection. A thorough review of contemporary techniques, methodologies, and devices utilized in IoT-based organic carbon detection in soil is presented. The paper not only sheds light on the current state-of-the-art but also explores the advantages, limitations, and prospects inherent in the implementation of IoT-enabled systems in agriculture. The envisioned outcomes encompass enhanced soil management practices, heightened crop yields, and the simultaneous preservation of the environment. In essence, this research contributes to the expanding body of knowledge surrounding IoT applications in agriculture, specifically emphasizing the transformative potential of IoT-enabled organic carbon detection for revolutionizing farming practices and promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings. [ABSTRACT FROM AUTHOR]

Continued population growth and limited land availability will facilitate the utilization of plant growth regulators (PGRs) in sustainable agriculture to enhance crop yields. The PGRs industry has progressed significantly from 2003 to 2022, resulting in a surge of research activities in the field of PGRs. However, the existing studies lack the exploration of the industry trends, as well as the challenges and opportunities for innovation in PGR development. Here, we analyze the dynamic trends within the PGR industry by examining key factors such as the PGR market, patent applications, scientific papers, and PGRs registrations from 2003 to 2022. Additionally, we investigate the specific effects of major agrochemicals on plants. These data will provide essential insights into the ongoing evolution and future trends of PGRs. Importantly, it is crucial to actively pursue research and development (R&D) of a broader range of PGRs to respond to the current needs of the PGR market and drive further growth therein. [ABSTRACT FROM AUTHOR]

Optimal foraging theory predicts that well‐defended potential foods should be exploited only when energy pay‐offs are great. Although stinging hymenopteran nests are both well‐defended and predated by primates, their larvae's energy yields rarely have been calculated, and predation‐linked foraging behaviours by primates infrequently documented. Based on 58 opportunistic observations of primates raiding wasp nests for larvae, we calculated energetic yields of low‐ and high‐risk wasp nest predation for Cebus albifrons, Saimiri collinsi, S. sciureus and Sapajus apella, and tested predictions derived from optimal foraging theory. We recorded how nests were processed and by which age‐sex classes, eaten nest fragment sizes, number of occupied and empty cells, and nest occupancy patterns (percent larvae/pupae, eggs, empty cells). Basal metabolic rate (BMR) calculations showed energetic yields from 15 min foraging on low‐risk nests (Polybia quadricincta) would meet energy needed to sustain adult female and male C. albifrons BMR for 4.9 and 4.5 h, respectively; yields from high‐risk (Chartergus artifex) nests for 6.5 and 6.2 h; Mischocyttarus sp. nest yields (low risk, but mimetically resembling other wasps) would meet S. collinsi BMR for 2.9 h (female) and 2.3 h (male), and 2.6 and 2.1 h, for the slightly larger S. sciureus, respectively. The Chartergus energetic‐yield value is nearly 20% of a 36 g chocolate bar (741 kJ). Our data provide quantitative support for the common assertion that wasp larvae and pupae are high‐yield foods for primates. As predicted by optimal foraging, energetic yield is sufficient to offset the risk and pain of being stung. [ABSTRACT FROM AUTHOR]

The growth of the world population is leading to an increased demand for food production. Crop yield prediction models are vital for agricultural planning and decision-making, providing forecasts that can significantly impact resource management and food security. This paper focuses on the importance and benefits of feature optimization in enhancing the performance of crop yield prediction models. By reducing noise and complexity, optimized features allow the prediction models to concentrate on the critical factors affecting crop yield, leading to more precise predictions and lesser computation times. This work utilizes an enhanced genetic algorithm to optimize feature selection and model parameters, outperforming the performance of standard genetic algorithms. Comparative analysis showed significant improvement in the accuracy of yield predictions by optimizing the selection of relevant features. The minimal error between actual and predicted yields on both the training and testing datasets highlights the effectiveness of the enhanced genetic algorithm. Enhanced feature optimization not only improves the robustness and adaptability of yield prediction models but also contributes to more sustainable and efficient agricultural management. [ABSTRACT FROM AUTHOR]

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms. [ABSTRACT FROM AUTHOR]

This paper discusses about the modern techniques implemented in the field of agriculture which shaped the traditional farming to the smart farming (Agriculture 4.0). The rapid rise in the global population is one among the main reasons, besides monitoring of crops health and yield requires a huge labor force, which is also another reason for promoting intelligent systems into agriculture sector. Conventional farming methods are not suitable to meet this demand, which led robotics to associate with on-field agriculture by means of robot-based technologies like wheeled robots, ground vehicles (manned and unmanned) and aerial vehicles (manned and unmanned) that led to explore the possible advancement in agriculture 3.0. Currently, the evolutionary techniques, such as Artificial Intelligence (AI) and IoT, are implemented in robotic vehicles to make them intelligent systems. Due to unprecedented climatic change and polluted ground water for the past few decades, the crops are being infested with new varieties of diseases. This requires new image processing techniques to classify the diseases based on color, texture and, shape of leaves. The incorporation of image processing technique into AI aids in deciding the appropriate amount of herbicide supplement to the plant based on the prediction of plant growth. [ABSTRACT FROM AUTHOR]

The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect fulltime sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP50-95, and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties. [ABSTRACT FROM AUTHOR]

Advanced molecular approaches for improving crop yield and quality lie in their comprehensive analysis of the latest advancements in molecular techniques that can be applied to enhance crop production. This review paper provides an in-depth analysis of the latest developments in advanced molecular approaches aimed at enhancing crop yield and quality. The paper comprehensively covers a range of techniques, including marker-assisted selection (MAS), genomic selection (CRISPR-Cas9, TALENs, and ZFNs), gene stacking and trait pyramiding, genome editing techniques, RNA interference (RNAi), epigenetic modifications, and multi-omics approaches. Each technique is thoroughly examined and evaluated, with particular attention given to their advantages and limitations, as well as their potential applications in crop improvement. Ultimately, this review can contribute to the development of more effective and sustainable approaches to crop improvement, which is crucial for ensuring food security and environmental sustainability in the face of an increasingly uncertain future. [ABSTRACT FROM AUTHOR]

MPROVING agricultural output worldwide to satisfy the demands of a rapidly expanding I population may be possible, in part, through the creation and use of novel fertilizers made possible by cutting-edge nanotechnology. As a matter of fact, according to the literature assessment, there are designed nanomaterials that, at specific concentrations, can improve plant development. These materials could be utilized as nanofertilizers in agriculture to boost crop yields and reduce pollution. Nanofertilizers that enhance plant development, nanofertilizers that include nutrients, and nanofertilizers that contain micronutrients are the four main types of nanomaterials discussed in this article. Concerning the chemical makeup of the nanomaterials, the size of the particles, the concentrations used, the plant species that benefited, the techniques of plant incubation, and the features and rates of plant growth promotion, each category is addressed separately. Furthermore, we take a close look at each type of nanofertilizer to determine its relevance, potential future paths, and special research needs in the pursuit of sustainable agriculture. Lastly, this paper argues that protecting the environment and increasing food production should be top research priorities, and they propose developing macronutrient nanofertilizers containing N and P. [ABSTRACT FROM AUTHOR]

For economic, environmental, and climatic reasons, soil cultivation methods without the use of a plough have became increasingly popular in recent decades. As research has shown, their impact on the growth and yield of crops is not always positive, especially when direct sowing is used. Recently, a new solution in the field of tillage has appeared which involves loosening the soil only within the sowing zone, which is called strip till. In the research presented in this work, this method was used for various cultivars of winter wheat on uncultivated stubble, but also after cultivation of the stubble just after harvest of a forecrop. The aim of the paper was to define the impact of the aforementioned factors on the parameters of photosynthesis, leaf area index, and water use efficiency. The range of soil cultivation after the harvest of the forecrop and cultivars influenced significantly the measured photosynthesis parameters. The impact of the cultivation method and variety on water use efficiency was small, although it should be noted that limiting the intensity of cultivation after the forecrop harvest had a temporary positive impact on the flag leaf phase. The winter wheat cultivar had an impact on the yield obtained: Formacja (0.44 t ha-1) and Metronom (0.37 t ha-1) yielded higher than Desamo. In the case of plough tillage, the yield was higher than in the case of minimum tillage and no tillage. Given the economic and environmental benefits associated with reduced fuel consumption, no tillage with a relatively slightly lower yield may be an alternative to the use of plough tillage. [ABSTRACT FROM AUTHOR]

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

Although wood biomass is mostly used to produce solid biofuel pellets, it is important to evaluate the possibilities of using other types of biomass as well. It is not only important to obtain biofuel pellets of suitable quality but also to ensure a sustainable process of producing and using these pellets for energy production. This paper presents an evaluation of the quality characteristics of seven different biofuel pellets made from multi-crop plants (fibrous hemp, maize, and faba bean) and a life cycle assessment (LCA) of the heat production by burning these pellets. The physical-mechanical properties and elemental composition of the pellets are determined according to international standards, as indicated in the methodology section. The LCA was performed using the SimaPro 9.5 software. The complete life cycle from cradle-to-grave is assessed, i.e., from growing plants to spreading ash obtained from pellet burning. An analysis showed that in most cases the produced pellets met the requirements of the standard ISO 17225-6:2021. The lowest negative environmental impact associated with the production of 1 GJ of thermal energy was for pellets made from fibrous hemp and maize biomass (MIX2-1) and pellets made from fibrous hemp and faba bean biomass (MIX2-3). Production of pellets from maize biomass (S-Mz) was found to have the highest carbon footprint (29.1 CO2eq GJ−1) and was associated with the lowest crop yield compared to the other six scenarios. [ABSTRACT FROM AUTHOR]

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This review presents a recent study on improving energy crops (ECs) in the EU and discusses the potential use of biostimulants to enhance CO2 sequestration processes in these plants. The novelty of this study lies in demonstrating alternative directions for improving productivity and increasing plant yield without relying on introducing new hybrids (GM) or using advanced agrotechnology. There is a great deal of discussion about using energy crops for direct combustion or biogas production. However, there is a paucity of information regarding the use of biostimulants and their role in increasing the yield of energy crops, particularly in terms of CO2 compensation. In CO2 sequestration, increasing the intensity of the photosynthetic process is considered crucial for the more efficient growth of energy crops. Traditionally, fertilization aimed at improving photosynthesis results in a large amount of alkaline elements, which can cause negative effects in boilers. This paper describes the use of amino acid biostimulants extracted from industrial waste, either chemically or biotechnologically, and their different forms of application. It addresses the current challenges and benefits of using biostimulants in energy crops to increase photosynthesis without the use of genetic engineering tools in plants. [ABSTRACT FROM AUTHOR]

Sustainability in cotton production is inevitable because producing more cotton means more employment, economic acceleration, and industrial expansion. India, China, the United States, Brazil, and Pakistan contribute 74% of worldwide cotton production. Pakistan is contributing only 5%, despite the high potential of cotton. The average yield of cotton in Pakistan is stagnant at 570.99 kg hm−2, whereas it entails the highest cost of production among all other crops. The yield obtained in Pakistan is less than the potential, profitability is drastically lessening, and farmers are abandoning cotton for alternative kharif crops. Some traditional quantitative studies have unveiled different factors that affect cotton production. However, an in-depth qualitative study has never been conducted in Pakistan to explore the root causes of growing cotton crop failure. Following Moustakas's traditional phenomenological guidelines, this phenomenological study was conducted in the district of Rahim Yar Khan in the core cotton zone of Punjab province. A total of 10 interviews were conducted with purposively selected cotton growers based on a criterion: (i) having more than 10 years of cotton growing experience, (ii) being a cotton grower, and (iii) having at least 10 years of formal schooling. Interviews were conducted face to face on an interview guide. One interview lasted 45–50 min, and responses were recorded and analyzed using a thematic analysis approach. A total of 6 themes emerged from the collected data, including (i) climate change, (ii) varietal problems, (iii) pesticide usage, (iv) sense of institutional services, (v) attitude of farmers and (vi) soil health and environment. These six merging themes contributed to cotton crop failure and yield decline. The deep exploration further summarized that researchers, extensionists, and farmers need to seriously consider variety, sowing time, and the environment to revive cotton crops. The detailed recommendations and policy guidelines are presented in this paper, highlighting the cotton sector's research, development and investment areas. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet-of-things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings. [ABSTRACT FROM AUTHOR]

Soil compaction is generally viewed as one of the most serious soil degradation problems and a determining factor in crop productivity worldwide. It is imperative to understand the processes involved in soil compaction to meet the future global challenges of food security. In this work, we used co‐occurring keyword analysis to summarize 3491 papers on soil compaction over the past 40 years, elaborating on the main research focuses such as the causes, influencing factors, and effects of soil compaction on crops, and the mitigation and prevention of soil compaction. This review provides a comprehensive discussion of the effects of soil compaction, including altering soil structure, increasing bulk density (BD) and penetration resistance (PR), and reducing porosity and soil hydraulic properties. Notably, based on the 387 data points of 11 papers about BD, our results demonstrated soil compaction on average, increased BD by 7.6%, 6.9%, and 3.2% in the medium‐, coarse‐, and fine‐textured soils, respectively. Based on the 264 data points of 18 papers, in the 0–30 cm soil layer, compaction increased penetration resistance (by 91% in the coarse‐textured, 84.2% in the medium‐textured, and 8.8% in the fine‐textured soils). Compacted soil limits the access of crop roots to water and nutrients, leading to poor root development and reduced crop productivity. There was a difference in soil compaction sensitivity between the different crops, but crop growth and yield showed an overall worsening trend with increasing degrees of compaction. This review collected data points on 142 crop yields and found that wheat, barley, corn, and soybean yields decreased by an average of 4.1%, 15.1%, 37.7%, and 22.7%, respectively, in the BD range of 1.1–1.8 Mg/cm3 after compaction. Additionally, the effectiveness of different compaction mitigation measures, including natural, tillage, and biological, is systematically discussed. Compared with soil compaction mitigation measures, prevention should be the top priority although there is still a lack of practical prevention methods. Soil conditions and agricultural machinery type are the main factors affecting the risk of soil compaction in the process of soil compaction. Therefore, it is particularly important to optimize the soil working conditions in the field and the type of farm machinery used to reduce the risk of soil compaction. This initiative is pivotal for ensuring sustainable systems for food production and recovering crop productivity from compacted soil. [ABSTRACT FROM AUTHOR]

[Objective] Winter wheat in Northern China is often rotated with other crops which are expected to have a legacy effect. This paper aims to study the effect of different tillage and preceding crops on soil nutrients, yield and water use efficiency of the winter wheat. [Method] The field experiment consisted of a rotary tillage (RT) and a no-tillage (NT). The preceding crops for each tillage were corn (MW) or soybean (SW). During the experiment, we measured the changes in soil nutrients, soil water content, water and fertilizer use efficiency, and yield of the wheat. [Result] For the same preceding crop, non-tillage increased soil water content compared to rotary tillage; under the same tillage, soil water content with maize as the preceding crop was higher than that with soybean as the preceding crop. In all treatments, soil nutrients decreased with soil depth. In the 0-50 cm soil layer, NTSW increased soil organic carbon, soil total nitrogen and nitrate nitrogen by 4.14%-13.54%, 35.51%-54.44% and 55.75%-112%, respectively, compared to the RTMW. RTMW had the highest available phosphorus and potassium in the 10-50 cm soil layer. The RTSW had the highest ammonium nitrogen in the 0-50 cm soil layer, 13.39%-20.64% more than that in the RTMW. The winter wheat in NTSW had the highest spike numbers, spikelet numbers, thousand-grain weight; and its grain yield was 16.62% higher than that of RTMW. The NTSW had the highest water use efficiency, 8.67% more than that of RTMW. The NTSW had the highest nitrogen, phosphorus and potassium use efficiency. [Conclusion] The NTSW facilitated nutrient accumulation in the winter wheat field. It had the highest water and fertilizer use efficiency and increased grain yields the most as a result. [ABSTRACT FROM AUTHOR]

The Haors in Bangladesh are saucer-shaped, low-lying land depressions that form deep basins; they remain submerged for approximately half of the year, typically from June onwards. This fragile ecosystem spans over 2.0 million hectares in the northeastern region of the country, accounting for roughly 14% of the total areas, where approximately 19.4 million people reside. Factors including floods, flash floods, and low winter temperatures constrain agricultural productivity in the haor areas. It is a great challenge to change the haor areas from less productive to more productive land. This is a comprehensive analysis of the biophysical and socioeconomic characteristics of haors which also highlights the constraints and opportunities in agricultural production. It explores strategies for significantly increasing crop, livestock, and fish production within the haor ecosystem, in alignment with government policies. Some of the proposed agricultural development strategies for the haor areas include the development of short-duration, cold-tolerant crop varieties, such as Boro rice, utilizing relatively flood-free elevated lands and homesteads for vegetable production and promoting agricultural mechanization, livestock rearing, fisheries, and agribusiness development. The recommendations presented in this paper focus on enhancing crop yields, increasing cropping intensity, and boosting livestock and fish production; ultimately, they contribute to food security, poverty reduction, and improved livelihoods for the inhabitants of the haor areas. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of crop planting units plays a critical role in crop yield estimation, soil management, food supplies, and disaster warnings. However, precisely mapping crop types is challenging in smallholder farming systems due to heterogeneous and mixed pixels, where field sizes are small, and crop types are very diverse. In this paper, the crop type system of Huocheng County in northwest China as an example area, crop classification feature variables are constructed using Sentinel-2 remote sensing images, and combining the ReliefF algorithm, three feature selection classification models are established by 3092 crop type sampling points data. The crop planting units are extracted using pixel-based and object-based classification methods, respectively, and unmanned aerial vehicle (UAV) data assess the accuracy as ground truth. The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. This is particularly evident when integrating this approach into pixel-based classification, addressing challenges such as low pixel classification accuracy in regions of this type. The validation results from UAV data also confirm these findings. At validation points 2, 3, and 4, pixel-based classification demonstrates significantly higher accuracy compared to object-based classification, with pixel-based classification accuracy improving by 9.16%, 7.83%, and 31.83%, respectively. Although the UAV validation accuracy does not reach a high level, pixel-based classification remains the optimal choice for smallholder crop classification. This method offers new insights and references for research related to the classification of smallholder crops with complex planting structures. [ABSTRACT FROM AUTHOR]

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model's viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert's pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers' pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included. [ABSTRACT FROM AUTHOR]

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. [ABSTRACT FROM AUTHOR]

In modern agriculture, nano-priming represents an innovative approach, harnessing the power of nanotechnology to enhance crop yields and nutrition. However, to effectively harness the potential of nanoparticles (NPs) for agriculture applications, understanding their mode of action and optimal application rates for positive effects on microgreen growth and physiology is critical. In this interdisciplinary study, we investigated the priming of sunflower seeds with a range of concentrations (25, 50, and 100 mg/L) of ferric oxide (Fe2O3) nanoparticles (FeNPs) and compared them with control samples. Our findings revealed a significant increase in plant biomass, leaf size, and photosynthetic activity in treated samples. The activities of photosystems I and II increased with higher FeNPs concentration. The treated samples exhibited elevated levels of total phenolics, anthocyanin, and antioxidant enzyme activity, along with increased macronutrients and micronutrients. These findings highlight the potential of FeNPs as a promising tool for enhancing plant growth and physiology in sunflower microgreens. [ABSTRACT FROM AUTHOR]

Agriculture, the foundation of human civilization, has relied on manual practices in the face of unpredictable weather for millennia. The contemporary era, however, witnesses the transformative potential of the Internet of things (IoT) in agriculture. This paper introduces an innovative IoT-driven smart agriculture system empowered by Arduino technology, making a significant contribution to the field. It integrates key components: a temperature sensor, a soil moisture sensor, a light-dependent resistor, a water pump, and a Wi-Fi module. The system vigilantly monitors vital environmental parameters: temperature, light intensity, and soil moisture levels. Upon surpassing 30°C, an automatic cooling fan alleviates heat stress, while sub-300CD light levels trigger light-emitting diode lighting for optimal growth. Real-time soil moisture data is relayed to the "Blynk" mobile app. Temperature thresholds align with specific crops, and users can manage the water pump via Blynk when manual intervention is required. This work advances agricultural practices, optimizing water management by crop type. Through precise coordination of soil moisture, temperature, and light intensity, the system enhances productivity while conserving water resources and maintaining fertilizer balance. [ABSTRACT FROM AUTHOR]

The use of slow and controlled release fertilizers (S/CRFs) has been beneficial in enhancing fertilizer utilization and crop yields, improving food security. However, the massive use of conventional S/CRFs has also polluted soil, water, and air, which poses a challenge to the sustainability of modern agriculture. To address this, a more efficient and environmentally-friendly material for crop nutrition is needed. Chitosan gels have proven to be an ideal carrier for nutrient transport given their biodegradability and high adsorption characteristics. This paper reviews the cross-linking of chitosan gels, preparation methods, nutrient transport, and performance, and how they contribute to sustainable agriculture. In addition, we detail the contribution of chitosan gels fertilizers to greenhouse gas reduction, soil conditioning, and crop yield improvement. In conclusion, although further research is needed on chitosan gels fertilizers as S/CRFs, it is expected that their use could lead to improved nutrient efficiency and reduced environmental pollution from a long-term development perspective. [ABSTRACT FROM AUTHOR]