Efficiency of traps in collecting selected Diptera families according to the used bait: Comparison of baits and mixtures in a field experiment

Traps made from PET bottles were used to assess the efficiency of four baits in terms of the number of individuals for selected Diptera families collecting in Eastern Slovak gardens in summer and autumn. Bait used in traps significantly affected the taxonomical composition of the samples obtained. Moreover, significant differences in bait efficiencies and temporal shift in bait efficiencies were confirmed for the Diptera order and for selected dipteran families. The most effective bait for baited-trap Diptera sampling was beer, followed by wine, meat, and syrup from the summer sampling season. In the autumn sampling season, the wine was most effective, followed by beer, syrup, and meat. For the family Scatopsidae wine, and for the family Platystomatidae, meat were the most effective baits. Drosophilidae were most attracted to beer in summer and to wine bait in autumn

Mamey (Mammea americana L.) in Martinique Island : un patrimonio para ser valorizados

Introduction. Mamey (Mammea americana L., Clusiaceae) was present in Martini-que before the Spanish colonization. Its distribution area includes tropical America and the Carib-bean. A significant phenotypical diversity is observed on the island, with fruits of very uneven quality as well as various agronomic, pomological and biochemical characteristics. The aim of our work was to localize, identify and characterize trees considered of superior quality. Materials and methods. A survey carried out between April and September 2005 allowed the selection of 10 trees renowned by the people as bearing high-quality fruits. These fruits present a small number of seeds and nonadhesive pulp, and develop a sweet taste as well as a strong flavor. During the year 2006, pomological description and biochemical analysis (total soluble solids and total titrable acidity) were carried out on the fruits. Results and discussion. The biometric and biochemical characteristics measured were generally better than those cited in the literature. Some accessions stand out and present great assets for their promotion for the fresh market as well as for processing. Moreover, some tendencies emerged from the variability observed for a few characters: thus, the variability of the biochemical characteristics measured within one accession, as well as between accessions originating from the same land, is low. It is null for the seed adhesion to the pulp for fruits belonging to the same accession. Conclusion and perspectives. Our work is one of the first relating to identification and characterization of phenotypical diversity of the M. americana L. species, especially in Martinique Island. Our results are likely to pro-mote the development of a diversification network. Some highlighted trends suggest new research to be able to distinguish the role of the environmental versus genetic components in the performance of the phenotypes observed

Insect bioactive capabilities of Epichloë festucae var lolii AR48 infected Lolium perenne : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Biochemistry at Massey University, Manawatū, New Zealand

Figures printed with permission from respective publishers.As the modern world expands and develops, new innovative methodologies for more efficient and environmentally friendly agricultural practices are required. Loss of crops through abiotic (e.g. drought) and biotic (e.g. herbivory) stresses has a major effect on the success of an agricultural industry. For animal production pasture crops are a key aspect of animal husbandry and directly affects yield and health. Symbiotic fungi belonging to the genus Epichloë form associations with cool season forage grasses and have been exploited as a new innovative method for insect pest management. Ryegrass infected with the asexual E. festucae var lolii strain AR48 has insect bioactivity against both the stem boring fly (SBF-Ceradontha australis) and cutworm moth caterpillar (CC -Agrotis ipsilion). The bioactive/s targeting both insects is currently unknown. The aim of this thesis was to identify the gene/s and/or bioactive/s present in AR48 infected ryegrass that have bioactivity against the SBF and/or CC. Two approaches were taken; the known insect bioactive secondary metabolite pathways in Epichloë were investigated in AR48 through bioinformatics and mass spectrometry, and the gene ‘makes caterpillars floppy’ (mcf), encoding an insect toxin like protein, was investigated through reverse genetics and insect bioactivity trials. A new indole diterpene compound (IDT) was identified in AR48 infected plant material and this compound was absent in other Epichloë strains that do not have SBF and CC bioactivity. The same mcf gene allele as that present in the E. typhina mcf model, previously identified as having CC bioactivity, is present and predicted to be functional in AR48. The other Epichloë strains also have mcf genes predicted to be functional, however the mcf allele is different to the bioactive E. typhina mcf model. Overall, this project was able to identify a new IDT compound with potential insect bioactivity as well as identify two Epichloë mcf gene alleles that potentially have differing insect bioactivities

Neural Network for Papaya Leaf Disease Detection

The scientific name of papaya is Carica papaya which is an herbaceous perennial in the family Caricaceae grown for its edible fruit. The papaya plant is tree-like,usually unbranched and has hollow stems and petioles. Its origin is Costa Rica, Mexico and USA. The common names of papaya is pawpaw and tree melon. In East Indies and Southern Asia, it is known as tapaya, kepaya, lapaya and kapaya. In Brazil,it is known as Mamao. Papayas are a soft, fleshy fruit that can be used in a wide variety of culinary ways. The possible health benefits of consuming papaya include a reduced risk of heart disease, diabetes, cancer, aiding in digestion, improving blood glucose control in people with diabetes, lowering blood pressure, and improving wound healing. Disease identification in early stage can increase crop productivity and hence lead to economical growth. This work deals with leaf rather than fruit. Images of papaya leaf samples, image compression and image filtering and several image generation techniques are used to obtain several trained data image sets and then hence providing a better product. This paper focus on the power of neural network for detecting diseases in the papaya. Image segmentation is done with the help of k-medoid clustering algorithm which is a partitioning based clustering method

Development of probabilistic models for quantitative pathway analysis of plant pest introduction for the EU territory

This report demonstrates a probabilistic quantitative pathway analysis model that can be used in risk assessment for plant pest introduction into EU territory on a range of edible commodities (apples, oranges, stone fruits and wheat). Two types of model were developed: a general commodity model that simulates distribution of an imported infested/infected commodity to and within the EU from source countries by month; and a consignment model that simulates the movement and distribution of individual consignments from source countries to destinations in the EU. The general pathway model has two modules. Module 1 is a trade pathway model, with a Eurostat database of five years of monthly trade volumes for each specific commodity into the EU28 from all source countries and territories. Infestation levels based on interception records, commercial quality standards or other information determine volume of infested commodity entering and transhipped within the EU. Module 2 allocates commodity volumes to processing, retail use and waste streams and overlays the distribution onto EU NUTS2 regions based on population densities and processing unit locations. Transfer potential to domestic host crops is a function of distribution of imported infested product and area of domestic production in NUTS2 regions, pest dispersal potential, and phenology of susceptibility in domestic crops. The consignment model covers the several routes on supply chains for processing and retail use. The output of the general pathway model is a distribution of estimated volumes of infested produce by NUTS2 region across the EU28, by month or annually; this is then related to the accessible susceptible domestic crop. Risk is expressed as a potential volume of infested fruit in potential contact with an area of susceptible domestic host crop. The output of the consignment model is a volume of infested produce retained at each stage along the specific consignment trade chain

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic

Farmers’ Preferences for the Design of Fruit Fly Pest-Free Area (FF-PFA) in Kerio-Valley: A Latent-Class Approach

Fruit flies are a very important group of pests for many countries due to their potential to cause damage in fruits thus restricting access to international markets for plant products that can host fruit flies. The high probability of introduction of fruit flies associated with a wide range of hosts’ results in restrictions imposed by many importing countries to accept fruits from areas in which these pests are established. For these reasons, establishment and maintenance of pest free areas for fruit flies (FF-PFAs) is receiving considerable attention in the current policy debates. Kenya Plant Health Inspectorate Service (KEPHIS) has taken the lead to establish and help maintain FF-PFAs in the main mango production zones of Elgeyo-Marakwet County of Kenya. However, as the ultimate success of the programme depends on farmers’ judgment and acceptance, acquiring information about potential demand is of paramount importance for policy advice. In this paper, we assess the demand in terms of consumer preferences and willingness to pay for FF-PFAs using a stated choice experiment method (SCE). A novel feature of this paper is that it focuses on how the FF-PFA should be designed and presented. Results from the latent class model (LCM) reveal that farmers prefer FF-PFAs featuring training, market information with sales contract, large benefits to other mango value-chain actors and when they are recommended by officials. Keywords: FF-PFA, SCE, LCM, Farmers’ preference, Mang

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars