The integration of GPS and visual navigation for autonomous navigation of an Ackerman steering mobile robot in cotton fields.

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth.

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing.

Evaluation of Inference Performance of Deep Learning Models for Real-Time Weed Detection in an Embedded Computer.

The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time. One of the driving factors for weed detection is to develop alternatives to herbicides, which are becoming less effective as weed species develop resistance. Advances in deep learning technology have significantly improved the robustness of weed detection tasks.

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton.

Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed.

The Effect of Tractor Speed and Canopy Position on Fungicide Spray Deposition and Peach Scab Incidence and Severity.

Peach scab, caused by , is a damaging disease of peach in the southeastern United States. Thus, fungicides are applied to reduce peach scab. Tractor speed was investigated as a variable affecting spray deposition and disease control in relation to volume applied.

Thrips counts and disease incidence in response to reflective particle films and conservation tillage in cotton and peanut cropping systems.

Feeding damage to seedling cotton and peanut inflicted by adult and immature thrips may result in stunted growth and delayed maturity. Furthermore, adult thrips can transmit (TSWV) to seedling peanut, which reduces plant growth and yield. The objective of this research was to assess the efficacy of inert particle films, calcium carbonate or kaolin, in combination with conservation tillage, to reduce adult and immature thrips counts in cotton and peanut crops.

A customized metal oxide semiconductor-based gas sensor array for onion quality evaluation: system development and characterization.

A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit mainly comprised a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel.

Changing a dangerous rural cultural tradition: a randomized control study of youth as extra riders on tractors.

Purpose: This study used a randomized control design to evaluate the effectiveness of AgTeen, an in-home, family-based farm safety intervention, in decreasing extra riding on tractors by youth. Having children as extra riders on tractors has deep roots in farm culture, but it can result in serious injury or death.

Methods: The study randomized 151 families into 3 groups: parent-led intervention (fathers taught their families about farm safety), staff-led intervention (staff members who were peer farmers taught families), and a no-treatment control.

Electroanalytical studies on green leaf volatiles for potential sensor development.

An electrochemical study for detecting green leaf plant volatiles from healthy and infected plants has been devised and tested. The electrocatalytic response of plant volatiles at a gold electrode was measured using cyclic voltammetry, amperometric current-time (i-t) analysis, differential pulse voltammetry (DPV) and hydrodynamic experiments. The sensitivity of the gold electrode in i-t analysis was 0.

Using insect sniffing devices for detection.

Emerging information about the ability of insects to detect and associatively learn has revealed that they could be used within chemical detection systems. Such systems have been developed around free-moving insects, such as honey bees. Alternatively, behavioral changes of contained insects can be interpreted by sampling air pumped over their olfactory organs.

Associative learning of odor with food- or blood-meal by Culex quinquefasciatus Say (Diptera: Culicidae).

The ability of many insects to learn has been documented. However, a limited number of studies examining associative learning in medically important arthropods has been published. Investigations into the associative learning capabilities of Culex quinquefasciatus Say were conducted by adapting methods commonly used in experiments involving Hymenoptera.

Behavioral monitoring of trained insects for chemical detection.

A portable, handheld volatile odor detector ("Wasp Hound") that utilizes a computer vision system and Microplitis croceipes (Cresson) (Hymenoptera: Braconidae), a parasitoid wasp, as the chemical sensor was created. Five wasps were placed in a test cartridge and placed inside the device. Wasps were either untrained or trained by associative learning to detect 3-octanone, a common fungal volatile chemical.