Healthy Crops Research Articles

The Floral Microbiome and Its Management in Agroecosystems: A Perspective.

Disease management is critical to ensuring healthy crop yields and is often targeted at flowers because of their susceptibility to pathogens and direct link to reproduction. Many disease management strategies are unsustainable however because of the potential for pathogens to evolve resistance, or nontarget effects on beneficial insects. Manipulating the floral microbiome holds some promise as a sustainable alternative to chemical means of disease control. In this perspective, we discuss the current state of research concerning floral microbiome assembly and management in agroecosystems as well as future directions aimed at improving the sustainability of disease control and insect-mediated ecosystem services.

Improvement on pest resistance of sprayable mulch films by fully utilizing garlic residues

Nutrient-rich liquid mulch films containing significant amounts of natural biomass inevitably attract pests, which hinder healthy crop development, despite the advantages of such mulch films in terms of degradability and low cost. To improve the pest resistance of mulch films derived from kitchen waste, we combined garlic residues, kitchen waste, and acrylic monomers to prepare sprayable garlic residue-acrylate liquid mulch films via emulsion polymerization. Garlic residues were fully utilized during the preparation process. The effects of the garlic residue dosage on the viscosity, sprayability, and wicking properties of the emulsions were, as well as the mechanics, water retention, and heat preservation of the garlic residue-acrylate films were studied. Microscopic, spectroscopic, and thermal analysis techniques were employed to characterize and monitor the biodegradability of the optimized garlic residue-acrylate films. The experimental results indicated that the film comprising a 3:7 wt ratio of garlic residues to acrylic monomers exhibited the best overall performance. The prepared garlic residue-acrylate mulch films effectively reduced soil water evaporation (by 2.1–12.3%) and increased soil temperature (by 1.26–3.75%). The optimized garlic residue-acrylate mulch film reached a biodegradation rate of 74.6% and increased the germination rate of pakchoi seeds by 20% relative to bare soil. Meanwhile, it reduced the degree of damage to pakchoi leaves by 11% and the survival rate of Plutella xylostella pests by 30% compared with the nutrient-rich film without garlic residues. The experimental results presented herein demonstrate that the full utilization of garlic residues can improve the performance of sprayable kitchen waste-based mulch films for agricultural applications.

Rice Leaf Disease Classification Using Cnn

Rice is amongst the majorly cultivated crops in India and its leaf diseases can have a substantial impact on output and quality. The most important component is identifying rice leaf diseases, which have a direct impact on the economy and food security. Brown spot, Leaf Blast, Hispa are the most frequently occurring rice leaf diseases. To resolve this issue, we have studied various machine learning and deep learning approaches for detecting the diseases on their leaves by calculating their accuracy, recall, and precision to measure the performance. This study helps the farmers by detecting the diseases in rice leaves in order to get a healthy crop yield. The deep learning models perform well when compared with the machine learning methods. After analyzing all of the deep learning models, we found that the 5-layer convolution model had the best accuracy of 78.2 %, while others, such as VGG16, had a lower accuracy of 58.4%.

Plant Disease Detection

Abstract: Plant growth is major requirement for framers, as it creates a path for their living, plants getting affected and their growth is related hand in hand. Framers strive to cultivate healthy crops; in spite of it plants getting affected are the major cause of crop failure. Plant disease is now the risk factor not only for framers but also to customers, environment and global economy. Immoderate pesticide usage is the cause for major health issues in plants. Plant disease detection using image processing can be the best way to predict and get accurate results. This project is based on deep convolutional neural networks which enhances the accuracy and training efficiency. This application will help many farmers who are uneducated to get correct information about diseases and help increase their yield. We are fostering a web application that can distinguish plant infection. The objective is to distinguish different plant infection by checking picture out. By utilizing CNN Algorithm we can identify the plant disease precisely. By the results of accuracy it shows this model is better than any traditional framing. Keywords: Plant Diseases, Deep Learning, Convolutional Neural Networks

Understanding Importance of Soil Testing and Significance of SOC on Crop Productivity

Healthy soils are important to grow healthy crops, raising healthy animals, and supporting a healthy human population through nutritionally balanced diets. As an indicator for soil health, SOC is important for its contributions to food production, mitigation and adaptation to climate change, and the achievement of the Sustainable Development Goals (SDGs). It supports multiple soil functions determining soil physical, chemical and biological quality parameters contributing to the productive capacity of soil. It improves soil structural stability by promoting aggregate formation which, together with porosity, ensures sufficient aeration and water infiltration to support plant growth. It is one of the factors that help in increasing water holding capacity, improves cation exchange capacity to hold huge quantities of positively charged nutrients such as calcium, magnesium and potassium until the plant needs them.

Whole-cell bacterial biosensor for volatile detection from Pectobacterium-infected potatoes enables early identification of potato tuber soft rot disease

Half of the harvested food is lost due to rots caused by microorganisms. Plants emit various volatile organic compounds (VOCs) into their surrounding environment, and the VOC profiles of healthy crops are altered upon infection. In this study, a whole-cell bacterial biosensor was used for the early identification of potato tuber soft rot disease caused by the pectinolytic bacteria Pectobacterium in potato tubers. The detection is based on monitoring the luminescent responses of the bacteria panel to changes in the VOC profile following inoculation. First, gas chromatography-mass spectrometry (GC-MS) was used to specify the differences between the VOC patterns of the inoculated and non-inoculated potato tubers during early infection. Five VOCs were identified, 1-octanol, phenylethyl alcohol, 2-ethyl hexanol, nonanal, and 1-octen-3-ol. Then, the infection was detected by the bioreporter bacterial panel, firstly measured in a 96-well plate in solution, and then also tested in potato plugs and validated in whole tubers. Examination of the bacterial panel responses showed an extensive cytotoxic effect over the testing period, as seen by the elevated induction factor (IF) values in the bacterial strain TV1061 after exposure to both potato plugs and whole tubers. Moreover, quorum sensing influences were also observed by the elevated IF values in the bacterial strain K802NR. The developed whole-cell biosensor system based on bacterial detection will allow more efficient crop management during postharvest, storage, and transport of crops, to reduce food losses.

CLEVER FARMING WITH CROPS DISEASE DETECTION SYSTEM USING IOT AND ML

Agriculture is the backbone of our country. Almost about 40% of annual global food supply loss is due to plant diseases. In developing countries, farmers generate 80% of agricultural products. For them, the loss of crops has a devastating consequence. Sometimes farmers lost 100% of crops due to some plant disease or illness. This makes crop illness a major threat to food security around the world. Identification of crop disease might be challenging due to the lack of lab infrastructure. Many IOT based manufacturing industries play an important role in the economic development of smart agriculture. Smart agriculture can include effective use of rain water, detection, and prevention of crops illness, detection and prevention of fire, prevention of animal intrusion in the field, etc by using different sensors and alarms. Crop disease is one of the most common problems which annoys farmers. So many times, farmers are not able to produce a good yield which results in less income for the farmer. This occurs due to various reasons like lack of minerals, soil humidity, changes in temperature, etc. Due to these parameters, illness in crops occurs which has an impact on product quality and quantity. IOT sensors can provide all necessary information to the farmers by properly monitoring the crops. Our goal is to create smart agriculture that utilizes the technologies like NodeMCU ESP8266, IOT, Android, WSN, and ML. The system monitors the temperature, humidity, wetness of the crops by using sensor NodeMCU ESP8266 and the farmer will receive a warning SMS on his smartphone using Wi-Fi. By using this proposed system farmers can easily detect the problemin the crop and this will help them to yield more healthy crops which will increase farmers' profit and food products in the country.

More Income, Less Pollution? How Income Expectation Affects Pesticide Application

Farmers are still the foundation of China’s current “small, scattered, and weak” agricultural production pattern. As such, increasing guidance for reduction response behavior is central to reducing agricultural pesticide use. Following this pesticide reduction logic, four of the most widely promoted pesticide reduction technologies, including light trapping, biopesticide application, healthy crop growth, and insect-proof net technologies, were selected, and a theoretical analysis framework of farmers’ willingness to adopt these technologies was constructed based on the theories of value perception and planned behavior. An ordered logistic regression model is used to explore key factors behind current pesticide reduction technology perceptions, technology response willingness, and behavioral decisions of farmers in China, with survey data from 516 farmers in Henan Province. The results show that among the four pesticide reduction technologies, healthy crop growth technology is the most-appealing one for farmers, followed by insect-proof net technology and biopesticide application technology. The least-appealing one for farmers is the light trapping technology. Farmers’ perceived degree of income improvement from technology adoption is the main determinant of their willingness, which is positively significant at a 1% confidence level in all four models. In addition, farmers’ willingness to respond to technologies is also significantly influenced by farmers’ perception of technical operational ability, perception of risk from adopting technology, government-related subsidies, government technical training guidance, trust in government promotion of technology, and perception of the government’s role in improving the external environment for adopting technology.

Why Using Biological Control Approaches Against Sugar Beet Aphid Pests Will Not Control Virus Yellows – a Post-script

As an advocate of natural pest control wherever possible, the author has no quarrel with Professor Loxdale?s appeal for research funders to consider enhancing the resources already available in the natural environment. In an ideal world, this would be the most acceptable method for producing healthy crops for our benefit, not to mention encouraging biodiversity within the landscape. However, in the real world, a more pragmatic approach is sometimes required to avoid catastrophic yield losses, such as those which occurred in sugar beet in the mid 1970s, and most recently in 2020, when it was estimated that 25% of sugar beet yields were lost to the aphid-transmitted yellowing viruses that infected 38% of the national sugar beet crop. Controlling up to 50 % of aphids by parasitoids or other natural enemies, whilst commendable in a situation where aphids might cause significant yield loss by direct-feeding, is certainly not sufficient to prevent widespread virus infection within crops, simply because it does not take many aphids to spread viruses. The threshold for aphid control using insecticide sprays in sugar beet in the UK is, and has been for decades, one green wingless aphid per four plants. This level of infection is actually quite difficult for a human to detect to make the judgement to use a spray or not, and it is a population level that does not attract many parasitoids or predators, even though they are better at finding them than we humans. But that threshold is necessary to help reduce the spread of the yield-reducing viruses and protect the yield of sugar beet.

Combining multiple Bacillus spp. with fish protein hydrolysates mitigates root rot (Fusarium solani) and improves cucumber seedlings growth and substrate nutrients.

The effect of Bacillus strains combined with fish protein hydrolysates (FPHs) on cucumber root rot disease, seedlings growth and substrate nutrients was investigated. We isolated three strains capable of mitigating cucumber root rot disease, XY-1 and XY-13strains were identified as B. amyloliquefaciens, and XY-53strain as B. subtilis. In the absence of bacteria, The 200×dilution (5mlL-1 ) of FPHs was the optimum concentration for improving cucumber seedlings growth. In vivo antibiosis tests showed that combined bacteria alongside FPHs inhibited the pathogen growth by 85%~90%, higher than individual bacteria. The FPHs combined either with XY-1 and XY-53strains or with XY-13 and XY-53strains promoted seedlings growth under infection, whereas FPHs combined with a mixture of XY-1, XY-13 and XY-53strains showed the highest total phosphorus and organic matter content in substrate. Moreover, FPHs combined with XY-53strain increased urease activity, while combined either with XY-13 and XY-53strains or with XY-1, XY-13 and XY-53strains increased sucrase activity under infection. FPHs combined with B. amyloliquefaciens and B. subtilis had great potential to suppress growth of root rot and promote cucumber seedlings and increase substrate nutrient content. Co-inoculation of B. amyloliquefaciens and B. subtilis with addition of FPHs is a good strategy for maintaining healthy crops.

CROP DISEASE DETECTION BY KERAS USING CONVOLUTION NEURAL NETWORK

Plants play a vital role in climate change, Agriculture industries, and a country's economy. Thereby taking care of plants is very decisive. Not only human beings but crops/plants are also carried out by several diseases caused by bacteria, fungi, and viruses. Pinpointing these diseases are take so much time and curing them is essential to prevent the whole crop from being devastated. This paper is focused on a deep learning model to dig out diseases on plant leaves. But, In the future model can be combined with a drone or any other system to live to detect diseases from plants and report these diseased plants’ location to farmers so that they can cure appropriately. All-important steps which are required to carry out disease recognition on plant leaves are explained in the paper. Healthy crop leaves and background images of corps are present in different classes, enabling the model to differentiate between not healthy leaves and healthy leaves from the environment by using the Convolution neural network.

An artificial intelligence solution for crop recommendation

Agriculture is the major occupation in India. The development of India is in the hands of farmers. Farmers are said to be our nation’s backbone, so there is a need to support our farmers technologically so that the difficulties of traditional agricultural practices would be overcome and also there will be positive impact on the yield, harvest, healthy crop output and the income of the farmers. Farmer needs awareness about his soil and the methods to improve his soil to grow the healthy crops. We propose an approach which involves deep learning and some IOT features to help our farmers. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it. We have developed a deep neural network model to predict the crop which can be suitably grown in the soil. We have also implemented the other machine learning classifiers on the same collected dataset to test the accuracies of each classifier and our deep neural network model.

Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles

Severe crop defoliation caused by insects and pests is linked to low agricultural productivity. If the root cause is not addressed, severe defoliation spreads, damaging whole crop fields. Understanding which areas are afflicted by severe defoliation can help farmers manage crops. Unmanned Aerial Vehicles (UAV) can fly over whole crop fields capturing detailed images. However, it is hard to characterize crop defoliation from aerial images that include multiple, overlapping plants with confounding effects from shadows and lighting. This paper assesses the efficacy of machine learning techniques to characterize defoliation. Given an UAV image as input, these techniques detect if severe defoliation is present. We created a labeled data set on soybean defoliation that comprises over 97,000 UAV images. We compared machine learning techniques ranging from Naive Bayes to neural networks and assessed their efficacy for (1) correctly characterizing images that contain defoliated crops and (2) avoiding wrong characterizations of healthy crops as defoliated. None of the techniques studied achieved high efficacy on both questions. However, we created DefoNet, a convolutional neural network designed for detecting crop defoliation that produces models that can be efficacious for either question. If adopted in practice, DefoNet models can guide decision making for mitigating crop yield losses due to defoliating insects.

Managing Thousand Cankers Disease in High-value Plantings of Black Walnut (Fagales: Juglandaceae) in Washington State

Abstract The health and productivity of black walnut (Juglans nigra L.) is currently threatened by Thousand Cankers Disease (TCD), a pest complex comprised of an insect vector, the walnut twig beetle (WTB) (Pityophthorus juglandis Blackman) (Coleoptera: Curculionidae), and associated pathogenic fungi (Geosmithia morbida Kolařík, Freeland, Utley, and Tisserat) (Hypocreales: Bionectriaceae). There is an urgent need for tools to manage TCD and avoid catastrophic losses in high-value plantations of black walnut. In this case study, we evaluated the efficacy of IPM strategies in three TCD-infested black walnut plantations in Walla Walla, WA. At the beginning of our study, average weekly captures of WTB per trap varied across sites; low (~32), moderate (~111), and high (>1,300). Each site received a separate management tactic: 1) girdled trap trees alone; 2) the insecticide emamectin benzoate alone or in combination with the fungicide propiconazole to protect healthy crop trees with superior form; and 3) the combination of girdled trap trees and chemically-protected crop trees. Walnut twig beetle populations remained at consistently low levels at both plantations after girdled trap trees were deployed. In plantations where crop trees were treated, the crown condition of those that received emamectin benzoate alone matched or exceeded that of trees treated with the combination of emamectin benzoate and propiconazole and the untreated control trees. Our findings suggest that using both trap trees and emamectin benzoate in an integrated approach has the most potential to reduce WTB populations and protect valuable crop trees in black walnut stands threatened by TCD.

Metal-Binding Oshipp16 Contributes to Detoxification and Lower Accumulation of Cadmium in Rice Associated with Healthy Crop Production and Food Safety

Metal-Binding Oshipp16 Contributes to Detoxification and Lower Accumulation of Cadmium in Rice Associated with Healthy Crop Production and Food Safety

Study on carbohydrate contents of potato by using biocontrol agent Pseudomonas fluorescens with farm yard manure

An attempt was made to study the bio-control efficacy of Pseudomonas flourescens on carbohydrate contents of potato crop .Three types of treatments; soil treatment, seed treatment and foliar treatment in combination with farm yard manure were given. The result was recorded at the time of harvest. The best result was recorded in soil treatment followed by foliar treatment and seed treatment in comparison with control. Therefore from present investigation, it is concluded that an eco-friendly biocontrol agent Pseudomonas fluorescens is very easy to use and having no adverse effect on crops, people or animals and they can be applied to prevent and control several pathogenic fungi and grow healthy crop. The Pseudomonas fluorescens can be used as a bio-control agent as it is low cost and profitable dependent system and it also helps in conserving the natural resource.

Wheat Diseases Detection and Classification using Convolutional Neural Network (CNN)

Ever since the medieval era, the preponderance of our concentration has been concentrated upon agriculture, which is typically recognized to be one of the vital aspects of the economy in contemporary society. This focus on agriculture can be traced back to the advent of the industrial revolution. Wheat is still another type of grain that, in the same way as other types of harvests, satisfies the necessity for the essential nutrients that are required for our bodies to perform their functions correctly. On the other hand, the supply of this harvest is being limited by a variety of rather frequent ailments. This is making it difficult to meet demand. The vast majority of people who work in agriculture are illiterate, which hinders them from being able to take appropriate preventative measures whenever they are necessary to do so. As a direct consequence of this factor, there has been a reduction in the total amount of wheat that has been produced. It can be quite difficult to diagnose wheat illnesses in their early stages because there are so many various forms of environmental variables and other factors. This is because there are numerous distinct sorts of agricultural products, illiteracy of agricultural workers, and other factors. In the past, a variety of distinct models have been proposed as potential solutions for identifying illnesses in wheat harvests. This study demonstrates a two-dimensional CNN model that can identify and categorize diseases that affect wheat harvests. To identify significant aspects of the photos, the software employs models that have previously undergone training. The suggested method can then identify and categorize disease-affected wheat crops as distinct from healthy wheat crops by employing the major criteria described above. The reliability of the findings was assessed to be 98.84 percent after the collection of a total of 4800 images for this study. These images included eleven image classes of images depicting diseased crops and one image class of images depicting healthy crops. To offer the suggested model the capability to identify and classify diseases from a variety of angles, the photographs that help compensate for the collection were flipped at a variety of different perspectives. These findings provide evidence that CNN can be applied to increase the precision with which diseases in wheat crops are identified.

A REVIEW ON CULTURAL PRACTICE AS AN EFFECTIVE PEST MANAGEMENT APPROACH UNDER INTEGRATED PEST MANAGEMENT

Integrated Pest Management is a sustainable and holistic approach based on ecological, biological, physical and chemical tactics for controlling pests in the field that, in any way, minimizes risk towards the health and environment. IPM methods involve identifying and knowing the characters of possible pests, making efforts to prevent prevalence, judgment of severity of pest-disease infestation through scouting and monitoring techniques. This paper highlights some of the already practiced strategies and experiments with interpretation of results obtained in various field conditions suggesting wider implications of IPM for managing pests in our fields. The review collects and studies some of the basic and commonly adopted cultural practices like clean and healthy crop growth, trap crop plantation, crop rotation, crop combinations etc. Cost effectiveness, ease of execution, instantly observable results etc are some unattainable aspects affecting IPM adoptions yet farmers seem somewhat encouraged towards sustainable and healthy production in recent years. It is necessary to know about crop and pest biology, ecology, phenology and their links/interaction to successfully implement cultural practices.

Comparison of a novel combination of bio-organic fertilizers vis-à-vis a chemical fertilizer

Plant nutrients are essential for the production of healthy crops for the world’s expanding population and thus, they are a vital component of sustainable agriculture. Increased crop production depends on the type of fertilizers used to supplement essential nutrients for plants. The nutrient level fertilizers provide depends on their nature- each type of fertilizer has its advantages and disadvantages concerning crop growth and soil fertility. The management of using fertilizer must aim to ensure both an enhanced and safeguarded environment. Thus, a balanced fertilization strategy must be implemented. An experiment was conducted under field conditions to assess the effects of combinations of bio-fertilizers on agronomic and quality criteria of Brassica juncea (brown mustard), Basella alba (climbing spinach), and Amaranthus dubius (red spinach). Randomized block design with three replicas were used for the study, one set with the application of fertilizers containing Azotobacter, Rhizobium, Sesbania, Bacillus subtilis, Bacillus cereus, Bacillus megaterium, Pseudomonas fluorescens and Glomus (Mycorrhizal inoculant)- under bio-fertilizer; another with a mixture of urea, Potassium Nitrate, Super Phosphate, Potassium Sulfate, and Maple EM solution as chemical fertilizer and a control (water). Results indicated that yield and other plant criteria like chlorophyll content and gel volume were enhanced in bio-fertilizer treated plants compared to the plants grown with chemical fertilizer and control. In general, the application of bio-fertilizer significantly increased leaf length by 16-50%, the total number of leaves by 50-80%, plant size 19.15-63.15%, and gel volume by 147% (approximately) in comparison with untreated plants.

SOIL HEALTH CHECKUP FOR BETTER CROP PRODUCTIVITY

Healthy soils produce healthy crops that in turn nourish people and animals. It is the foundation of the food system. Soil testing is a well recognized scientific method for quick characterization of the soil fertility status and prediction of crop nutrient requirement which are lost through improper use of fertilizers, continuous cropping, erosion, leaching, volatilization, denitrification and fixation. Soil Health Card (SHC) is a Government of Indias scheme which apart from giving the health index of soil & also indicates fertilizer recommendations and soil amendment required for the farm. Knowing the fertility status of soil before sowing of crop will lead to proper usage & dosage of appropriate fertilizer, thus maintaining the essential nutrients profile & OC status for optimal crop productivity.