The Role of Harvest Timing in Pest Management: Grower Response to Infestation by the California Olive Fruit Fly

Crop Production/Industries,

GIS based Infestation Biogeography of Palm Weevils, Pachymerus cardo in the Niger Delta, Nigeria

Data on Oil Palm Kernel Borer Pachymerus cardo were compiled from several sites in the Niger Delta at different Euclidean distances ranging from 11-127 kilometers. The monthly infestation rates and location similarity in infestation were examined with univariate non-spatial statistics and geographic information geostatistical tools. The results of the within month abundance of the weevils showed an average and peak infestation rate of (452:800)  in May with a slow increase through the months of June(497:921) to peak values in August (616:1272). This dropped to 578:1206 and 525:924 in September and October respectively. No significant differences within-month mean scores for all the months (p < 0.05) by both the student t and Hsu pair comparisons were observed. Significant differences from univariate non-spatial statistics were however observed in site infestation distribution (p < 0.05) between  Obiozimini, Omerelu,  Ebubu, Omoku, Egbeda and the following sites namely Abuloma, Obowo, Mbieri, Isiala Ngwa, Kaiama, Old  Bakana and between Egbeda and Obiozomini. A scaling of the sites using geostatistics techniques of kriging and inverse distance weighting indicated that geographic association among sites was independent of nearness or distance decay. A spatial autocorrelation analysis at a z-score of 2.98 and 95% confidence rejected the null hypothesis of the cause for the observed distribution to be random. The clustered nature of the weevil pest is attributed to the stable habitat and wide spread of the oil palm which is thriving within the same environmental variables of rainfall and humidity among the sites from where the weevils were collected. Key words. Oil Palm, Weevil, Pachymerus cardo, GIS, Autocorrelatio

Weight Management for the Elderly Population Who have Sustained a Lower Limb Amputation: Resource Manuals for Clinicians

It is becoming more common for individuals to sustain a lower limb amputation, thus impacting their ability to participate in functional tasks of daily living such as ambulation, balance, dressing, driving, and toileting. Approximately two million people within the United States are living with an amputation, most commonly in the lower extremity (Amputation Coalition, 2013). According to Resnik and Borgia (2011), by the year 2050, the number of lower limb amputations will increase significantly due to the aging population who encounter a variety of debilitating diseases such as diabetes, peripheral artery disease (PAD), dysvascular, and/or heart diseases. Weight management is an issue a significant percentage of individuals struggle with. According to the National Center for Health Statistics (2012), 69.2 percent of Americans are overweight. This trend in weight gain is also present in the amputee population and significantly affects the quality of life for these individuals because it is more strenuous on their remaining joints. For those who have a lower limb amputation, the daily battle with weight issues impacts the way the prosthetic fits the affected limb. If an individual is overweight, there is more fatty tissue within their affected limb, thus preventing the prosthetist from firmly applying the socket to the limb. Whereas, for those who have firmer muscles and less fatty tissue, they are able to have a better fit of their prosthetic socket; therefore, giving patients, better control, support, and stability for daily activities (Kahle & Highsmith, 2008). Significant research has been conducted regarding the various aspects of rehabilitation, strengthening, and community reintroduction for elderly clients who have undergone a lower limb amputation. However, these studies have not addressed the need for weight management as an essential aspect in this populations’ life. Therefore, the purpose of this project is to explore and develop a weight management resource for clinicians working with the elderly client who has experienced a lower limb amputation. These resources will consist of nutritional and fitness manuals to help individuals continually manage their weight to ensure their prosthetic device fits properly on a consistent basis. Through extensive research of evidence-based literature, the authors identified key concepts used to develop a product that benefits the elderly population who have a lower limb amputation. With the concepts identified through the literature review, two manuals were produced to guide clinicians on proper implementation of therapeutic exercises and nutritional aspects of weight management to assist their patients. The manuals also contain client handouts, allowing the clients to take the procedures home with them to continue maintaining their weight management routine. The information provided within the manuals was meant to assist elderly individuals develop lifelong routines, habits, and roles to promote independence and quality of life. The anticipated results of this project are to assist clinicians in helping the elderly who have sustained a lower limb amputation maintain their weight on a regular basis, and ensure a consistent fit of their prosthetic device. This will then enable clients to participate more fully in daily occupations with increased volition and performance capacity

Defining and Evaluating a Decision Support System (DSS) for the Precise Pest Management of the Mediterranean Fruit Fly, Ceratitis capitata, at the Farm Level

A Decision Support System (DSS) was developed and evaluated to control the Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedermann), by incorporating a semi-automatic pest monitoring and a precision targeting approach in multi-varietal orchards. The DSS consists of three algorithms. DSS1, based on the degree days calculation, defines when the traps should be deployed in the field initiating the medfly population monitoring. DSS2 defines the areas to be treated and the type of treatment based on the number of adult medfly captures, harvesting time, and phenological stage of the host cultivar. DSS3 defines the spraying procedure considering the technical registration properties of the selected insecticide (e.g., withholding period and efficacy duration time) and weather conditions. The DSS was tested in commercial orchard conditions near Rome, central Italy, with a randomized complete blocks experimental design, comparing DSS-assisted and conventional management. In the DSS-assisted plots, a semi-automatic adult medfly monitoring system was deployed, composed of real-time, wireless electronic traps. The output of the functioning DSS is a map of spraying recommendation, reporting the areas to be treated and the treatment type (bait or cover insecticide spraying). The farmer was left free to follow, or not, the DSS indications. The first medfly captures were observed on June 30, whereas the DD threshold was reached on July 3 when the DSS started to operate. The field test produced 29 DSS decisions from July 3 to September 1 and confirmed that medfly management using the DSS substantially reduced the number of pesticide applications, the treated area, and the volumes of pesticide utilization. No significant differences in infested fruit were observed between DSS-assisted and conventional management. The level of acceptance of the DSS by the farmer was 78%. This evidence confirmed the requirement of fully involving farmers and pest managers during the evaluation process of DSS

8th International Symposium on fruit flies of economic importance

Sabater Muñoz, B.; Urbaneja García, A.; Navarro Llopis, V. (2010). 8th International Symposium on fruit flies of economic importance. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/11200Archivo delegad

Common seasonal pests : your handy guide to prevent the spread of animal and plant pests, diseases and weeds.

This bulletin provides information on quarantine, common pests and common household pests in Western Australia. Details include identification, damage caused, seasonal occurance of pest, action to take, control measures, and where to seek advice.https://researchlibrary.agric.wa.gov.au/bulletins/1041/thumbnail.jp

ChatGPT in the context of precision agriculture data analytics

In this study we argue that integrating ChatGPT into the data processing pipeline of automated sensors in precision agriculture has the potential to bring several benefits and enhance various aspects of modern farming practices. Policy makers often face a barrier when they need to get informed about the situation in vast agricultural fields to reach to decisions. They depend on the close collaboration between agricultural experts in the field, data analysts, and technology providers to create interdisciplinary teams that cannot always be secured on demand or establish effective communication across these diverse domains to respond in real-time. In this work we argue that the speech recognition input modality of ChatGPT provides a more intuitive and natural way for policy makers to interact with the database of the server of an agricultural data processing system to which a large, dispersed network of automated insect traps and sensors probes reports. The large language models map the speech input to text, allowing the user to form its own version of unconstrained verbal query, raising the barrier of having to learn and adapt oneself to a specific data analytics software. The output of the language model can interact through Python code and Pandas with the entire database, visualize the results and use speech synthesis to engage the user in an iterative and refining discussion related to the data. We show three ways of how ChatGPT can interact with the database of the remote server to which a dispersed network of different modalities (optical counters, vibration recordings, pictures, and video), report. We examine the potential and the validity of the response of ChatGPT in analyzing, and interpreting agricultural data, providing real time insights and recommendations to stakeholdersComment: 33 pages, 21 figure

Usando Kinect como sensor para una pulverización inteligente.

Este trabajo esta orientado a resolver el problema de la caracterización de la copa de arboles frutales para la aplicacion localizada de fitosanitarios. Esta propuesta utiliza un mapa de profundidad (Depth image) y una imagen RGB combinadas (RGB-D), proporcionados por el sensor Kinect de Microsoft, para aplicar pesticidas de forma localizada. A través del mapa de profundidad se puede estimar la densidad de la copa y a partir de esta información determinar qué boquillas se deben abrir en cada momento. Se desarrollaron algoritmos implementados en Matlab que permiten además de la adquisición de las imágenes RGB-D, aplicar plaguicidas sólo a hojas y/o frutos según se desee. Estos algoritmos fueron implementados en un software que se comunica con el entorno de desarrollo "Kinect Windows SDK", encargado de extraer las imágenes desde el sensor Kinect. Por otra parte, para identificar hojas, se implementaron algoritmos de clasificación e identificación. Los algoritmos de clasificación utilizados fueron "Fuzzy C-Means con Gustafson Kessel" (FCM-GK) y "K-Means". Los centroides o prototipos de cada clase generados por FCM-GK fueron usados como semilla para K-Means, para acelerar la convergencia del algoritmo y mantener la coherencia temporal en los grupos generados por K-Means. Los algoritmos de clasificación fueron aplicados sobre las imágenes transformadas al espacio de color L*a*b*; específicamente se emplearon los canales a*, b* (canales cromáticos) con el fin de reducir el efecto de la luz sobre los colores. Los algoritmos de clasificación fueron configurados para buscar cuatro grupos: hojas, porosidad, frutas y tronco. Una vez que el clasificador genera los prototipos de los grupos, un clasificador denominado Máquina de Soporte Vectorial, que utiliza como núcleo una función Gaussiana base radial, identifica la clase de interés (hojas). La combinación de estos algoritmos ha mostrado bajos errores de clasificación, rendimiento del 4% de error en la identificación de hojas. Además, estos algoritmos de procesamiento de hasta 8.4 imágenes por segundo, lo que permite su aplicación en tiempo real. Los resultados demuestran la viabilidad de utilizar el sensor "Kinect" para determinar dónde y cuándo aplicar pesticidas. Por otra parte, también muestran que existen limitaciones en su uso, impuesta por las condiciones de luz. En otras palabras, es posible usar "Kinect" en exteriores, pero durante días nublados, temprano en la mañana o en la noche con iluminación artificial, o añadiendo un parasol en condiciones de luz intensa