Different extractable pools of Cd and Pb in agricultural soil under amendments: Water-soluble concentration sensitively indicates metal availability.

Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive, hindering accurate assessment on environmental risks and effectiveness of remediation strategies. This study evaluated the feasibility of European Community Bureau of Reference (BCR) sequential extraction, Ca(NO3)2 extraction, and water extraction on assessing Cd and Pb availability in agricultural soil amended with slaked lime, magnesium hydroxide, corn stover biochar, and calcium dihydrogen phosphate. Moreover, the enriched isotope tracing technique (112Cd and 206Pb) was employed to evaluate the aging process of newly introduced Cd and Pb within 56 days' incubation. Results demonstrated that extractable pools by BCR and Ca(NO3)2 extraction were little impacted by amendments and showed little correlation with soil pH. This is notable because soil pH is closely linked to metal availability, indicating these extraction methods may not adequately reflect metal availability. Conversely, water-soluble concentrations of Cd and Pb were markedly influenced by amendments and exhibited strong correlations with pH (Pearson's r: -0.908 to -0.825, P < 0.001), suggesting water extraction as a more sensitive approach. Furthermore, newly introduced metals underwent a more evident aging process as demonstrated by acid-soluble and water-soluble pools. Additionally, water-soluble concentrations of essential metals were impacted by soil amendments, raising caution on their potential effects on plant growth. These findings suggest water extraction as a promising and attractive method to evaluate Cd and Pb availability, which will help provide assessment guidance for environmental risks caused by heavy metals and develop efficient remediation strategies.

Combining slow-release fertilizer and plastic film mulching reduced the carbon footprint and enhanced maize yield on the Loess Plateau.

Agricultural practices significantly contribute to greenhouse gas (GHG) emissions, necessitating cleaner production technologies to reduce environmental pressure and achieve sustainable maize production. Plastic film mulching is commonly used in the Loess Plateau region. Incorporating slow-release fertilizers as a replacement for urea within this practice can reduce nitrogen losses and enhance crop productivity. Combining these techniques represents a novel agricultural approach in semi-arid areas. However, the impact of this integration on soil carbon storage (SOCS), carbon footprint (CF), and economic benefits has received limited research attention. Therefore, we conducted an eight-year study (2015-2022) in the semi-arid northwestern region to quantify the effects of four treatments [urea supplied without plastic film mulching (CK-U), slow-release fertilizer supplied without plastic film mulching (CK-S), urea supplied with plastic film mulching (PM-U), and slow-release fertilizer supplied with plastic film mulching (PM-S)] on soil fertility, economic and environmental benefits. The results revealed that nitrogen fertilizer was the primary contributor to total GHG emissions (≥71.97%). Compared to other treatments, PM-S increased average grain yield by 12.01%-37.89%, water use efficiency by 9.19%-23.33%, nitrogen accumulation by 27.07%-66.19%, and net return by 6.21%-29.57%. Furthermore, PM-S decreased CF by 12.87%-44.31% and CF per net return by 14.25%-41.16%. After eight years, PM-S increased SOCS (0-40 cm) by 2.46%, while PM-U decreased it by 7.09%. These findings highlight the positive effects of PM-S on surface soil fertility, economic gains, and environmental benefits in spring maize production on the Loess Plateau, underscoring its potential for widespread adoption and application.

Research progress on the environmental risk assessment and remediation technologies of heavy metal pollution in agricultural soil.

Controlling heavy metal pollution in agricultural soil has been a significant challenge. These heavy metals seriously threaten the surrounding ecological environment and human health. The effective assessment and remediation of heavy metals in agricultural soils are crucial. These two aspects support each other, forming a close and complete decision-making chain. Therefore, this review systematically summarizes the distribution characteristics of soil heavy metal pollution, the correlation between soil and crop heavy metal contents, the presence pattern and migration and transformation mode of heavy metals in the soil-crop system. The advantages and disadvantages of the risk evaluation tools and models of heavy metal pollution in farmland are further outlined, which provides important guidance for an in-depth understanding of the characteristics of heavy metal pollution in farmland soils and the assessment of the environmental risk. Soil remediation strategies involve multiple physical, chemical, biological and even combined technologies, and this paper compares the potential and effect of the above current remediation technologies in heavy metal polluted farmland soils. Finally, the main problems and possible research directions of future heavy metal risk assessment and remediation technologies in agricultural soils are prospected. This review provides new ideas for effective assessment and selection of remediation technologies based on the characterization of soil heavy metals.

Global suitability and spatial overlap of land-based climate mitigation strategies.

Land-based mitigation strategies (LBMS) are critical to reducing climate change and will require large areas for their implementation. Yet few studies have considered how and where LBMS either compete for land or could be deployed jointly across the Earth's surface. To assess the opportunity costs of scaling up LBMS, we derived high-resolution estimates of the land suitable for 19 different LBMS, including ecosystem maintenance, ecosystem restoration, carbon-smart agricultural and forestry management, and converting land to novel states. Each 1 km resolution map was derived using the Earth's current geographic and biophysical features without socioeconomic constraints. By overlaying these maps, we estimated 8.56 billion hectares theoretically suitable for LBMS across the Earth. This includes 5.20 Bha where only one of the studied strategies is suitable, typically the strategy that involves maintaining the current ecosystem and the carbon it stores. The other 3.36 Bha is suitable for more than one LBMS, framing the choices society has among which LBMS to implement. The majority of these regions of overlapping LBMS include strategies that conflict with one another, such as the conflict between better management of existing land cover types and restoration-based strategies such as reforestation. At the same time, we identified several agricultural management LBMS that were geographically compatible over large areas, including for example, enhanced chemical weathering and improved plantation rotations. Our analysis presents local stakeholders, communities, and governments with the range of LBMS options, and the opportunity costs associated with scaling up any given LBMS to reduce global climate change.

Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the growth, yield and quality of wolfberry. This study aimed to develop new spectral indices (NSIs) and constructed machine learning regression (MLR) models for predicting wolfberry tree LNC. By utilizing four smoothing methods and five mathematic transformation methods, we obtained the original spectral dataset and five spectral transformation datasets for quantitative analysis and model establishment. Subsequently, published vegetation indices (PVIs) were acquired, sensitive wavelengths (SWs) were screened and NSIs were calculated based on SWs. Then MLR models were constructed by combining NSIs from six spectral datasets with three machine learning algorithms. Finally, a comparison was made among the MLR models. The study indicated that the application of mathematical transformation highlighted the differences in spectra, the square root, first-derivative and second-derivative transformation improved the prediction accuracy of MLR models constructed based on NSIs (MLR-NSIs models). However, these transformations had little impact on improving the prediction ability of MLR models constructed based on PVIs (MLR-PVIs models). Additionally, The optimal model for predicting the LNC of wolfberry tree was obtained by using the Random Forest (RF) algorithm combined with NSIs developed by first-derivative transformation spectra. The determination coefficient of validation (Rv2) and ratio of percentage deviation (RPD) was 0.71 and 1.90, respectively. In conclusion, this study has demonstrated that the combination of hyperspectral transformation and machine learning is useful for improving the accuracy of LNC estimation in wolfberry trees.

Control of inorganic and organic phosphorus molecules on microbial activity, and the stoichiometry of nutrient cycling in soils in an arid, agricultural ecosystem.

Background: The dynamics of carbon (C), nitrogen (N), and phosphorus (P) in soils determine their fertility and crop growth in agroecosystems. These dynamics depend on microbial metabolism, which in turn depends on nutrient availability. Farmers typically apply either mineral or organic fertilizers to increase the availability of nutrients in soils. Phosphorus, which usually limits plant growth, is one of the most applied nutrients. Our knowledge is limited regarding how different forms of P impact the ability of microbes in soils to produce the enzymes required to release nutrients, such as C, N and P from different substrates. Methods: In this study, we used the arable layer of a calcareous soil obtained from an alfalfa cropland in Cuatro Cienegas, México, to perform an incubation experiment, where five different phosphate molecules were added as treatments substrates: three organic molecules (RNA, adenine monophosphate (AMP) and phytate) and two inorganic molecules (calcium phosphate and ammonium phosphate). Controls did not receive added phosphorus. We measured nutrient dynamics and soil microbial activity after 19 days of incubation. Results: Different P molecules affected potential microbial C mineralization (CO2-C) and enzyme activities, specifically in the organic treatments. P remained immobilized in the microbial biomass (Pmic) regardless of the source of P, suggesting that soil microorganisms were limited by phosphorus. Higher mineralization rates in soil amended with organic P compounds depleted dissolved organic carbon and increased nitrification. The C:N:P stoichiometry of the microbial biomass implied a change in the microbial community which affected the carbon use efficiency (CUE), threshold elemental ratio (TER), and homeostasis. Conclusion: Different organic and inorganic sources of P affect soil microbial community structure and metabolism. This modifies the dynamics of soil C, N and P. These results highlight the importance of considering the composition of organic matter and phosphate compounds used in agriculture since their impact on the microbial activity of the soil can also affect plant productivity.

Internet of things (IoT) based saffron cultivation system in greenhouse.

Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs.

Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.

The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).

Enzyme Inhibitors as Multifaceted Tools in Medicine and Agriculture.

Enzymes are molecules that play a crucial role in maintaining homeostasis and balance in all living organisms by catalyzing metabolic and cellular processes. If an enzyme's mechanism of action is inhibited, the progression of certain diseases can be slowed or halted, making enzymes a key therapeutic target. Therefore, identifying or developing enzyme inhibitors is essential for treating significant diseases and ensuring plant defense against pathogens. This review aims to compile information on various types of enzyme inhibitors, particularly those that are well studied and beneficial in both human and plant contexts, by analyzing their mechanisms of action and the resulting benefits. Specifically, this review focuses on three different types of enzyme inhibitors that are most studied, recognized, and cited, each with distinct areas of action and potential benefits. For instance, serine enzyme inhibitors in plants help defend against pathogens, while the other two classes-alpha-glucosidase inhibitors and carbonic anhydrase inhibitors-have significant effects on human health. Furthermore, this review is also intended to assist other researchers by providing valuable insights into the biological effects of specific natural or synthetic inhibitors. Based on the current understanding of these enzyme inhibitors, which are among the most extensively studied in the scientific community, future research could explore their use in additional applications or the development of synthetic inhibitors derived from natural ones. Such inhibitors could aid in defending against pathogenic organisms, preventing the onset of diseases in humans, or even slowing the growth of certain pathogenic microorganisms. Notably, carbonic anhydrase inhibitors have shown promising results in potentially replacing antibiotics, thereby addressing the growing issue of antibiotic resistance.

Mixed effectiveness of global protected areas in resisting habitat loss.

Protected areas are the cornerstones of conservation efforts to mitigate the anthropogenic pressures driving biodiversity loss. Nations aim to protect 30% of Earth's land and water by 2030, yet the effectiveness of protected areas remains unclear. Here we analyze the performance of over 160,000 protected areas in resisting habitat loss at different spatial and temporal scales, using high-resolution data. We find that 1.14 million km2 of habitat, equivalent to three times the size of Japan, across 73% of protected areas, had been altered between 2003 and 2019. These protected areas experienced habitat loss due to the expansion of built-up land, cropland, pastureland, or deforestation. Larger and stricter protected areas generally had lower rates of habitat loss. While most protected areas effectively halted the expansion of built-up areas, they were less successful in preventing deforestation and agricultural conversion. Protected areas were 33% more effective in reducing habitat loss compared to unprotected areas, though their ability to mitigate nearby human pressures was limited and varied spatially. Our findings indicate that, beyond establishing new protected areas, there is an urgent need to enhance the effectiveness of existing ones to better prevent habitat loss and achieve the post-2020 global biodiversity goals.

Context-dependent agricultural intensification pathways to increase rice production in India.

Yield gap analysis is used to characterize the untapped production potential of cropping systems. With emerging large-n agronomic datasets and data science methods, pathways for narrowing yield gaps can be identified that provide actionable insights into where and how cropping systems can be sustainably intensified. Here we characterize the contributing factors to rice yield gaps across seven Indian states, with a case study region used to assess the power of intervention targeting. Primary yield constraints in the case study region were nitrogen and irrigation, but scenario analysis suggests modest average yield gains with universal adoption of higher nitrogen rates. When nitrogen limited fields are targeted for practice change (47% of the sample), yield gains are predicted to double. When nitrogen and irrigation co-limitations are targeted (20% of the sample), yield gains more than tripled. Results suggest that analytics-led strategies for crop intensification can generate transformative advances in productivity, profitability, and environmental outcomes.

Plastic pollution in agricultural landscapes: an overlooked threat to pollination, biocontrol and food security.

Ecosystem services such as pollination and biocontrol may be severely affected by emerging nano/micro-plastics (NMP) pollution. Here, we synthesize the little-known effects of NMP on pollinators and biocontrol agents on the organismal, farm and landscape scale. Ingested NMP trigger organismal changes from gene expression, organ damage to behavior modifications. At the farm and landscape level, NMP will likely amplify synergistic effects with other threats such as pathogens, and may alter floral resource distributions in high NMP concentration areas. Understanding exposure pathways of NMP on pollinators and biocontrol agents is critical to evaluate future risks for agricultural ecosystems and food security.

Mapping climate suitability index for rainfed cultivation of medicinal plants by developing an AI-based probabilistic framework.

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability.

Crop calendar optimization for climate change adaptation in yam farming in South-Kivu, eastern D.R. Congo.

The traditional crop calendar for yam (Dioscorea spp.) in South-Kivu, eastern Democratic Republic of Congo (DRC), is becoming increasingly inadequate given the significant climatic variability observed over the last three decades. This study aimed at: (i) assessing trends in weather data across time and space to ascertain climate change, and (ii) optimizing the yam crop calendar for various South-Kivu agro-ecological zones (AEZs) to adapt to the changing climate. The 1990-2022 weather data series were downloaded from the NASA-MERRA platform, bias correction was carried out using local weather stations' records, and analyses were performed using RClimDex 1.9. Local knowledge and CROPWAT 8.0 were used to define planting dates for yam in different AEZs. Results showed the existence of four AEZs in the South-Kivu province, with contrasting altitudes, temperatures, and rainfall patterns. Climate change is real in all these South-Kivu's AEZs, resulting either in rainfall deficits in some areas, or extreme rainfall events in others, with significant temperature increases across all AEZs. Suitable yam planting dates varied with AEZs, September 15th and 20th were recommended for the AEZ 2 while October 15th was optimal for AEZ 1, AEZ 3, and AEZ 4. However, none of the planting date scenarios could meet the yam water requirements in AEZ1, AEZ3, and AEZ4, since the effective rainfall (Pmm) was always inferior to the plant water demand (ETc), meaning that soil water conservation practices are needed for optimum plant growth and yield in these AEZs. This study does not recommend planting yam during the short rainy season owing to prolonged droughts coinciding with critical growth phases of yam, unless supplemental irrigation is envisaged. This study provided insights on the nature of climate change across the past three decades and suggested a yam crop calendar that suits the changing climate of eastern DRC.

Unraveling the characteristics of microplastics in agricultural soils upon long-term organic fertilizer application: A comprehensive study using diversity indices.

Microplastics negatively impact soil health and productivity. Organic fertilizers constitute significant contributors of microplastics in agricultural soils. Nevertheless, comprehensive data on the diversity of microplastics in long-term fertilized soils remain unavailable. In this study, we assessed the presence of microplastics in soils subjected to application of three different organic fertilizers (pig manure, chicken manure, and sludge composts) over 12 years, and evaluated the potential ecological risks posed by microplastic accumulation. The average microplastic abundance in soil was 368.88 ± 207.97 (range: 90-910) items/kg. Microplastic abundance differed among fertilization treatments, with substantial increases of 16.67%, 71.67%, and 61.43% upon low to high application of the three treatments, respectively. Overall, the microplastics predominantly comprised fibers (70.94%) and fragments (25.25%), of which a substantial proportion constituted light-colored microplastics (transparent and white). The size of microplastics was mainly concentrated in the 1-2 mm range (39.96%), with rayon, polypropylene, polyester, and polyethylene being identified as the major types. The risk assessment indices of the three treatments were 229.38, 257.64, and 175.89, respectively, and were all classified as level 4 (high risk). The microplastic diversity integrated index and principal component analysis revealed that microplastics were uniformly distributed throughout the 0-20 cm soil depth consequent to tillage activity. Together, these findings provide a comprehensive assessment of microplastic pollution in long-term fertilized soils and serve as a scientific basis for reducing microplastic contamination in agricultural soils.

A comprehensive review of antibiotic resistance gene contamination in agriculture: Challenges and AI-driven solutions.

Since their discovery, the prolonged and widespread use of antibiotics in veterinary and agricultural production has led to numerous problems, particularly the emergence and spread of antibiotic-resistant bacteria (ARB). In addition, other anthropogenic factors accelerate the horizontal transfer of antibiotic resistance genes (ARGs) and amplify their impact. In agricultural environments, animals, manure, and wastewater are the vectors of ARGs that facilitate their spread to the environment and humans via animal products, water, and other environmental pathways. Therefore, this review comprehensively analyzed the current status, removal methods, and future directions of ARGs on farms. This article 1) investigates the origins of ARGs on farms, the pathways and mechanisms of their spread to surrounding environments, and various strategies to mitigate their spread; 2) determines the multiple factors influencing the abundance of ARGs on farms, the pathways through which ARGs spread from farms to the environment, and the effects and mechanisms of non-antibiotic factors on the spread of ARGs; 3) explores methods for controlling ARGs in farm wastes; and 4) provides a comprehensive summary and integration of research across various fields, proposing that in modern smart farms, emerging technologies can be integrated through artificial intelligence to control or even eliminate ARGs. Moreover, challenges and future research directions for controlling ARGs on farms are suggested.

Can pyrolysis and composting of sewage sludge reduce the release of traditional and emerging pollutants in agricultural soils? Insights from field and laboratory investigations.

The potential extractability, crop uptake, and ecotoxicity of conventional and emerging organic and metal(loid) contaminants after the application of pre-treated (composted and pyrolysed) sewage sludges to two agricultural soils were evaluated at field and laboratory scale. Metal(loid) extractability varied with sludge types and pre-treatments, though As, Cu, and Ni decreased universally. In the field, the equivalent of 5 tons per hectare of both composted and pyrolysed sludges brought winter wheat grain metal(loid) concentrations below statutory limits. Carbamazepine, diclofenac, and telmisartan were the only detected organic pollutants in crops decreasing in order of root > shoot > grains, whilst endocrine-disrupting chemicals, such as bisphenol A and perfluorochemicals were heavily reduced by composting (up to 71%) or pyrolysis (up to below detection limit) compared to raw sludges. As a consequence, no detectable concentrations were measured in soils 12 months after field application. This study highlights the potential advantages of processing sewage sludge before soil applications, especially in the context of reducing the mobility of emerging contaminants, though further studies are required on a broad range of soils and crops before land application can be considered.

Streamlining YOLOv7 for Rapid and Accurate Detection of Rapeseed Varieties on Embedded Device.

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.

Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture.

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers' hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers' hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand-wrist-forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity.

Early and late blight disease identification in tomato plants using a neural network-based model to augmenting agricultural productivity.

Computer-advanced technologies have a significant impact across various fields. It is widely recognized that diseases have a detrimental effect on crop productivity and can significantly impact the economy, particularly in agricultural countries. Tomatoes hold great economic importance among cash crops, second only to potatoes. Globally, tomato production reaches a staggering 160 million tons annually, making it even more crucial for agricultural development. Unfortunately, the tomato crop is susceptible to several diseases, with early blight and late blight as two prominent culprits responsible for a production decrease of around 79%. Traditional disease detection and identification methods are time-consuming, expensive, and destructive, often requiring pathologists' expertise. Thus, the primary research objective is to enhance disease identification accuracy by leveraging deep learning techniques. A model based on the inception-V3 architecture has been devised to classify diseases affecting tomato plant leaves. The model was trained and tested using the PlantVillage dataset, which comprises 6000 sample images of tomato leaves. The training and testing process utilized an 80 : 20 ratio, resulting in an impressive classification accuracy of 97.44% for the proposed model. The proposed solution aims to enable the tomato industry to thrive in the global market by mitigating the impact of tomato leaf diseases. By reducing the prevalence of these diseases, the solution can increase demand and contribute to the industry's growth.