Healthy Crops Research Articles

Genetic Potential of Sesame: A Study on Variability, Heritability and Genetic Advance

Aims: This study aimed to evaluate 140 genotypes of sesame germplasms along with five check varieties to assess heritability, variability, and genetic advance for yield and yield-related agronomic characters, with the goal of improving yield potential through breeding programs. Study Design: The evaluation was conducted using an augmented design with four blocks during the kharif season of 2021. Methodology: The investigation took place at the S.K.N. College of Agriculture's institutional farm during the kharif season of 2021, utilizing 140 sesame germplasm lines and five check varieties from ICAR-National Bureau of Plant Genetic Resources. The germplasm was evaluated in an Augmented Design with each line planted in a 4-meter row, organized across four blocks, and standard agronomic practices were followed to ensure healthy crop development. Results: Significant differences among the germplasm lines were observed for days to 50 percent flowering, days to maturity, plant height, capsule-bearing length, capsules per plant, seeds per capsule, branches per plant, capsule length, chlorophyll content, test weight, and seed yield. This indicates substantial variability among the germplasm lines. Traits such as seed yield per plant, capsules per plant, and primary branches per plant exhibited high phenotypic and genotypic coefficients of variation. High heritability coupled with high genetic advance as a percentage of the mean was noted for seed yield per plant, capsules per plant, primary branches per plant, and test weight, suggesting the influence of additive gene action, thereby making simple selection effective for improving these traits. Conclusion: The evaluation of sesame germplasms revealed significant variability for various yield and agronomic traits, with traits like seed yield per plant, capsules per plant, and primary branches per plant showing potential for improvement through breeding programs. High heritability and genetic advance suggest the influence of additive gene action, facilitating effective selection strategies for enhancing yield potential in sesame.

Wheat Rust Disease Detection Using Convolutional Neural Network

Wheat is a vital crop globally, playing a crucial role in ensuring food security. Any damage to wheat crops, particularly from diseases like wheat rust, can exacerbate the global food crisis. Wheat rust, along with other crop diseases, poses a serious threat to agricultural sustainability and food supply. Technological approaches to detect crop health can significantly enhance disease prevention compared to traditional manual methods. This study focuses on detecting wheat rust diseases, specifically wheat leaf rust, stem rust, and yellow rust, all of which vary in their impact on crops. By employing image analysis of wheat plants, the research aims to accurately distinguish between healthy crops and those affected by rust diseases. The experiments consider multiple variables, such as learning rate, dropout, and train-test split ratio, reflecting a comprehensive research approach. The model achieved an impressive 99.64% accuracy in detecting wheat rust, highlighting its potential for early disease detection and prevention.

Exploring possibilities of broccoli (Brassica oleracea L. var. italica) production under organic management

Broccoli (Brassica oleracea L. var. italica) was introduced as part of the Indian government's efforts to diversify and modernize agriculture in the region. Its introduction has helped J&K farmers to increase their income through higher market prices and to gain access to new markets and trade opportunities. It being a nutrient-demanding crop, farmers use high doses of chemical fertilizers, pesticides and herbicides and if consumed as salad or half-boiled in many culinary dishes, these chemicals are likely to impact the health of consumers and affect the environment. Therefore, alternative usage to these chemicals using combinations of organic inputs was aimed at producing a healthy crop of broccoli. It was concluded that the application of FYM@ 25 t/ha as a basal dose in the presence of seedling dip treatment of biofertilizers (Azotobacter @ 0.5 kg/ha + Phosphate-Solubilizing Bacteria @l kg/ha + Potassium-Mobilizing Bacteria @ 750 ml/ha) along with the scheduled application of Neem oil +Baeuveria bassiana at 1, 3, 7 and 15 days of interval after disease symptoms was found superior in obtaining higher curd yield (109.57 q/ha) and seed yield (604.96 kg/ha).

TRENDS OF GERMINATION IN CHILLI SEEDS

The germination of chilli seeds has traditionally been impacted by physiological or endogenous dormancy traits. Low seed germination rates and vigor growth are issues that could contribute to the poor seedling development of the chilli crop. The most common treatment solution, gibberellic acid (GA3), is not a novel technique for breaking the dormancy of chili seeds. However, further research is required to determine the possible impact of this treatment on the germination of chilli seeds and the growth of seedlings, including the pursuit of an improved GA3 concentration. Thus, the purpose of this study was to assess how various GA3 concentrations affected the performance of seedling growth and emergence in chilli. FGP and GRI had maximum value for T2, and all the treatments were highly significant for FGP and significant for GRI. In T3, the germination performance of the seed decreased drastically. MGT (MGT) decreased from control to T2 and then increased in T3. All three treatments were significantly different for MGT. SH and SVI had maximum values for T2, and all three treatments were significantly different from each other for SVI and highly significant for SH. The optimum concentration of GA3 as pretreatment of chilli seed will help better germination and healthy crop in turn.

Portable X‐ray fluorescence spectrometry accurately measures metal concentrations in aqueous Mehlich III soil extraction solutions

AbstractAccurate measurement of metal concentrations in soil and water is vital for healthy crop production and decision making for environmental surveys. While there are a multitude of laboratory‐based soil analysis methods, such as inductively coupled plasma‐optical emission spectroscopy (ICP‐OES), flame emission spectrometry, and atomic absorption spectroscopy, most are time and resource intensive. Additionally, there is a lack of information for rapid analysis of elements for aqueous soil extractions. The goal of this research is to establish elemental correlations between portable X‐ray fluorescence (pXRF) measurements of Mehlich III soil extractions and traditional elemental measurements via ICP‐OES. We hypothesize that certain metals can be accurately measured in aqueous soil extraction solutions by pXRF to the same degree as they are measured by ICP‐OES. To test this hypothesis, Mehlich III and 2% nitric acid solutions with known elemental concentrations were analyzed via ICP‐OES and pXRF. Soil samples extracted using Mehlich III were compared between ICP‐OES and pXRF to verify correlations. High correlations were found for As, Ca, Cd, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, V, and Zn in both the Mehlich III and 2% nitric acid solutions at concentrations between 5 and 85 mg L−1. P, S, and Si did not show high correlations at concentrations <100 mg L−1. These results indicate that between 5 and 85 mg L−1, pXRF analysis of aqueous solutions and soil extractions is a reliable technique; however, at low concentrations (i.e., <5 mg L−1 for metals and <100 mg L−1 for P and S), pXRF is not well suited.

Review on Different Types of Leaf Disease Detection Methods

This review explores the various methods employed in detecting leaf diseases, highlighting their significance in ensuring healthy crop yields. The paper examines traditional techniques like visual inspection and microscopy, as well as advanced methods that leverage machine learning, image processing, and deep learning algorithms. Emphasis is placed on the accuracy, efficiency, and scalability of these approaches, considering the challenges of real-time disease detection in diverse environmental conditions. By comparing the strengths and limitations of different methods, the review aims to provide a comprehensive understanding of the current state and future directions in leaf disease detection technology.

Influence of Sowing Techniques, Timing of Herbicide Application and on Weed Infestation and Yield of Rice in Southern Guinea Savannah of Nigeria

Field trial was conducted at the research field of National Cereals Research Institutes Badeggi experimental field, Niger state located at (latitude 9o041 02.05’’N and longitude 0.6o011 3.31’’E) and Bacita located at (latitude 9o1 16’N and longitude 5o59’E) both in the Southern Guinea Savannah Agro Ecological Zone of Nigeria during the wet season of 2021 and 2022. To determine the best combination of sowing techniques and time of herbicide application for optimizing rice yield while minimizing weed infestation and crop injury in the Southern Guinea Savannah of Nigeria. The experiment consists of five sowing methods (broadcasting dry, broadcasting wet, dibbling wet, dibbling dry, and transplanting) and two herbicide application timings (2 and 3 weeks after sowing) were tested in a factorial experiment. Data were collected on weed count before herbicide application, weed count at harvest and grain yield per hectare. The result indicated that the Transplanting technique of sowing significantly improved rice productivity in the study area at both seasons, less weed infestation at harvest was recorded with transplanted methods compared to other panting techniques, herbicide application at early growth stage of 2 weeks after sowing (2WAS) promote healthy crop growth and minimize weed competition

CNN ensemble approach for early detection of sugarcane diseases – a comparison

This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision.

Plant clinic in Nepal: An overview

Plant clinics play an important role in supporting farmers in growing healthy crops and achieving higher productivity in Nepal. The development and operation of plant clinics in Nepal are assessed in this study through a comprehensive analysis of the institution via literature review and interaction with key stakeholders. The plant clinic approach of the agriculture extension system started in Nepal in 2008, followed up with engagement with CABI and the Government of Nepal. Enhancement of farmers' knowledge and skill, encouraging sustainable farming methods, and ultimately increasing crop yields are the impact of plant clinics. However, there are some challenges faced during the implementation of plant clinics in the existing agricultural extension systems. In Nepal, the plant clinics are primarily operated by agricultural technicians with expertise in plant protection, as well as IPM farmer facilitators and community business facilitators after attending an intensive plant doctor's training. Plant clinics have been integrated into the agricultural extension system by agro-advisory service provider of the government of Nepal. Despite institutionalisation, policy support needs to be strengthened to ensure the sustainability of the different components of the (e.g., data management, validation, monitoring, localised content, etc.) plant clinic in Nepal.

A Systematic Review of Artificial Intelligence: A Future Guide to Sustainable Agriculture

The population is expected to grow rapidly and reach 10 billion people by the year 2050. As a result, there will be a greater need for food. The conventional techniques employed by the farmers proved insufficient to meet these demands. For this, new automated techniques were unveiled. Along with other cutting-edge computer science applications, farming has long made use of technologies like artificial intelligence. The focus has shifted in recent years to consider the applications of this new technology. A significant percentage of humanity's nutrition has come from agriculture for thousands of years, with the most significant contribution being the broad adoption of efficient farming techniques for a variety of crops. The application of artificial intelligence (AI) in agriculture has sparked a revolution in the field, and AI technology has made the agro-based commercial sector operate more profitably. Artificial intelligence (AI) technologies have the power to transform the future and address problems. This will make it easier for farmers to learn about climate variance and pests that lower crops. The use of AI focuses on identifying damaged crops and enhancing the ability of healthy crops to provide higher yields. This paper gives a thorough analysis of AI models used in agricultural applications. It also examines the use of AI models to specify sustainable goals. This article explores the challenges and opportunities for utilizing AI to develop future generations of sustainable agriculture through this comprehensive review.

SOIL HEALTH ANALYSIS AND CLASSIFICATION OF MICRONUTRIENTS FOR CROP SUGGESTION USING MACHINE LEARNING

An essential component of agriculture is soil. Many types of soil exists. Different parameters can be observed in each type of soil, and various crops can be grown on different types of soils. To understand which crops develop in certain types of soil, they need to be informed on the characteristics and features of various soil types. The capability to identify the inputs needed for productive and cost-effective farming was provided by soil analysis. A proper soil test will ensure that the proper amount of fertilizers are utilized, providing the crop to meet its needs while also profiting from the nutrients already available in the soil. The phrase "soil health" refers to the biological, chemical, and physical elements of the soil that are significant for plant nutrients. Soil analysis is a collection of different chemical processes that determines the amounts of available plant nutrients in the soil. The most important factors for producing a healthy crop are micronutrient analysis. The classification of soil's micronutrients is the primary objective of this analysis. In this case, machine learning methods may be helpful. It has made significant progress in recent years. Hence in this analysis, soil health analysis and classification of Micronutrients for crop suggestion using Machine Learning is presented. The coloured images of the soil samples are obtained and processed using a number of algorithms and filters. Color, texture, and other features can be extracted using these developed algorithms. Logistic Regression algorithm is used to classify micronutrients.

Enhancing nutritional status, growth, and fruit quality of dried figs using organic fertilizers in rain-fed orchards: A case study in Estahban, Iran.

The majority of Iranian fig production is exported, making it one of the world's most well-known healthy crops. Therefore, the main objective of the current experiment was to investigate the effects of various types of organic fertilizers, such as animal manure (cow and sheep), bird manure (partridge, turkey, quail, and chicken), and vermicompost, on the nutritional status of trees, vegetative and reproductive tree characteristics, fruit yield, and fruit quality traits in dried fig cultivar ("Sabz"). According to the findings, applying organic fertilizers, particularly turkey and quail, significantly improves vegetative and reproductive characteristics. However, other manures such as sheep, chicken, and vermicompost had a similar effect on the growth parameters of fig trees. Additionally, the findings indicated that except for potassium, use of all organic fertilizers had an impact on macro and microelements such as phosphorus, nitrogen, and sodium amount in fig tree leaves. Also, based on fruit color analysis in dried figs, the use of all organic fertilizers improved fruit color. Moreover, the analyses fruit biochemical showed that the use of some organic fertilizers improved that TSS and polyphenol compounds such as coumarin, vanillin, hesperidin gallic acid and trans frolic acid. In general, the results indicated that the addition of organic fertilizers, especially turkey manure, led to increased vegetative productivity and improvement in the fruit quality of the rain-fed fig orchard.

Crop Analyzer Using Deep Learning and Yolo

Abstract: This paper presents an AI-driven crop analysis system using deep learning, integrated into a user-friendly web app built with Flask. Convolutional neural networks (CNNs) are used by the model, which was trained on a variety of crop photos, to accurately classify the crop images into categories such as healthy crops and different stages of disease. Crop health may be easily monitored by farmers and agronomists thanks to the user-friendly website. The system's practical applications, such as precision agriculture and disease management, hold promise for enhancing global food security by automating crop disease detection and minimizing losses

Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The progress made in deep learning techniques has significantly impacted agricultural disease diagnostics. Rice Plant leaf diseases can have severe adverse effects on crop yield, and proper diagnosis of Rice Plant diseases is vital to avoid these effects. Most of the existing methods do not adequately detect the leaf images and cannot find the condition accurately. Previously, there were some problems with leaf segmentation. Complex disease stages are efficient but computationally time-consuming, and segmentation needs to be more accurate, affordable, and reliable. To overcome the issues, the proposed method of Deep Spectral Generative Adversarial Neural Network (DSGAN2) with Improved Artificial Plant Optimization for Rice Plant leaf disease detection. Initially, we fed into the input of healthy and non-healthy leaves from the collected dataset. Then, we apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the Closest Weight from segmented Rice Plant leaves. Social Spider Optimization to select the Feature with the Closest Weight (S2O-FCW) algorithm for analysis of the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects Rice Plant disease based on selected features. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

Selecting a stirrer and optimizing the process of mixing the substrate in the fermenter of biogas unit

The relevance of the work is related to the problem of soil degradation as a result of pollution by livestock waste and the need for timely disposal of waste into effective organic fertilizer and biogas. Increasing the share of the use of organic fertilizers makes it possible to develop organic farming technologies, and accordingly leads to the production of healthy crop products. The work investigated the process of substrate mixing in the fermenters of an operating pilot biogas plant. The technological line is designed for processing liquid organic waste, mainly manure or litter, by anaerobic digestion to produce innovative environmentally friendly organic fertilizers on an industrial scale. The purpose of the study was to select a stirrer and optimize the conditions for mixing the substrate in the fermenters of a biogas plant. The physicochemical properties of the substrate were studied by laboratory methods. The mathematical model of the mixing process is based on a semi-empirical description of the hydrodynamics of flows in the reactor, modernized taking into account the current state of the issue under the conditions of a fermenter with specific geometric characteristics. As a result of the work carried out, the optimal options for the geometric dimensions of the stirrers were determined (three variants for three-blade stirrers intermittent with a blade angle of 30°, differing in the size of the impeller and rotation speed) and the parameters of the mixing process were calculated. Based on a laboratory experiment and the results of calculating the sedimentation rate, the frequency of turning on the stirrer (once a day) and the time of its continuous operation to homogenize the suspension were determined. The results of the work can be used in the design of mixing devices and substrate mixing modes in bioreactors.

CoDet: A novel deep learning pipeline for cotton plant detection and disease identification

Cotton detection is a crucial component of the agricultural sector because it enables farmers to correctly identify and keep track of the development of cotton crops. Systems for automatically detecting cotton could boost output and efficiency while decreasing costs and waste in cotton growing operations. New cotton detection systems have been developed as a result of recent developments in machine learning and computer vision. These devices can precisely identify and monitor cotton plants using images and sensor data. These systems assess and categorize cotton plants according to their many spectral signatures using convolutional neural networks (CNNs), deep learning algorithms, and hyperspectral imaging, among other methods. The use of cotton detection technologies can help with problems related to crop diseases, pests, and environmental factors in addition to enhancing crop management and production optimization. Farmers and researchers may spot possible issues early and take corrective action to decrease risks and promote healthy crop growth by offering real-time monitoring and data analytics. As cotton detecting technologies have the potential to alter the cotton farming sector and improve environmentally friendly farming techniques, they represent a promising area for research and development. The proposed pipeline demonstrates how cotton may be recognized quickly and reliably.

Trichothecenes and Fumonisins: Key Players in Fusarium-Cereal Ecosystem Interactions.

Fusarium fungi produce a diverse array of mycotoxic metabolites during the pathogenesis of cereals. Some, such as the trichothecenes and fumonisins, are phytotoxic, acting as non-proteinaceous effectors that facilitate disease development in cereals. Over the last few decades, we have gained some depth of understanding as to how trichothecenes and fumonisins interact with plant cells and how plants deploy mycotoxin detoxification and resistance strategies to defend themselves against the producer fungi. The cereal-mycotoxin interaction is part of a co-evolutionary dance between Fusarium and cereals, as evidenced by a trichothecene-responsive, taxonomically restricted, cereal gene competing with a fungal effector protein and enhancing tolerance to the trichothecene and resistance to DON-producing F. graminearum. But the binary fungal-plant interaction is part of a bigger ecosystem wherein other microbes and insects have been shown to interact with fungal mycotoxins, directly or indirectly through host plants. We are only beginning to unravel the extent to which trichothecenes, fumonisins and other mycotoxins play a role in fungal-ecosystem interactions. We now have tools to determine how, when and where mycotoxins impact and are impacted by the microbiome and microfauna. As more mycotoxins are described, research into their individual and synergistic toxicity and their interactions with the crop ecosystem will give insights into how we can holistically breed for and cultivate healthy crops.

PRANAM-Ca: An Eggshell Derived Calcium Fertilizer and Its Impact on Blossom end Rot in Tomato Cultivation

Eggshells, amounting to a staggering 76.7 million tons annually, are often inadequately disposed of in landfills, leading to significant nutrient loss for both plants and humans, while contributing to environmental pollution and emitting unpleasant odors. In response to these challenges, recent research has been dedicated to repurposing eggshells, exploring their applications in diverse fields such as biochemistry, biomedicine, environmental technology, and agriculture. Recognized as an exceptional source of calcium, eggshells offer notable benefits for calcium-demanding crops, including tomatoes, chilli and cotton. In a comprehensive study conducted, we investigated the application of organic foliar calcium derived from eggshells on tomato plants. The results demonstrated substantial improvements in addressing physiological disorders, promoting overall growth, and enhancing yield, signifies the importance of calcium fertilizer for growing healthy crop. With this background, Multiplex R&D team has made an effort to develop an in-house technology to derive the calcium from waste eggshell. Afterwards, how to scale-up and commercialise it as a sprayable form of Organic calcium fertilizer, PRANAM-CA. Present paper aims to describe the technique to derive calcium from eggshell and its impact on managing the blossom end rot in Tomato cultivation under protected glass-house condition.

Evaluation of deep learning techniques for plant disease detection

In recent years, several proposals have been based on Artificial Intelligence techniques for automatically detecting the presence of pests and diseases in crops from images usually taken with a camera. By training with pictures of affected crops and healthy crops, artificial intelligence techniques learn to distinguish one from the other. Furthermore, in the long term, it is intended that the tools developed from such approaches will allow the automation and increased frequency of plant analysis, thus increasing the possibility of determining and predicting crop health and potential biotic risks. However, the great diversity of proposed solutions leads us to the need to study them, present possible situations for their improvement, such as image preprocessing, and analyse the robustness of the proposals examined against more realistic pictures than those existing in the datasets typically used. Taking all this into account, this paper embarks on a comprehensive exploration of various AI techniques leveraging leaf images for the autonomous detection of plant diseases. By fostering a deeper understanding of the strengths and limitations of these methodologies, this research contributes to the vanguard of agricultural disease detection, propelling innovation, and fostering the maturation of AI-driven solutions in this critical domain.

A novel rice plant leaf diseases detection using deep spectral generative adversarial neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN2) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S2O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects rice plant disease based on selected features. With an accuracy of 97 %, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.