Improved determination of the optimum maturity of maize based on Alexnet

The increase in the number of humans and animals, particularly livestock in Sub-Sahara Africa without a correspondent increase in land resources has led to shortages, and consequently metamorphose into unhealthy clashes between farmers and herders. The unpredictable changes in climatic conditions in recent times and human activities has also contributed to deforestation and desertification. The maize plant is being considered to mitigate for the shortage by the application of Computational Intelligence technique and image processing in the determination of the optimum maturity of the maize. There are different varieties of maize that are quite suitable for different climatic conditions in Sub-Saharan Africa. In this paper, the optimum maturity of SAMMAZ 17 variety of maize seedling is selected due to its high resilient to drought, striga condition and its good composition of nutrients. The maturity is determined by the application of Alexnet on 3000 samples of maize comb captured at different maturity stages cultivated in the same farm land. The network gave an accuracy of 72.44%. The result obtained showed a 4.44% improvement over an earlier result obtained by the use of Resnet-50. The finding is a window of opportunity for improvement in the determination of the optimum maturity of maize

Методы определения цветовых характеристик растительного сырья. Обзор

Food product quality defines a complex of food product properties such size, shape, texture, color and others, and determines acceptability of these products for consumers. It is possible to detect defects in plant raw materials by color and classify them by color characteristics, texture, shape, a degree of maturity and so on. Currently, the work on modernization of color control systems has been carried out for rapid and objective measuring information about color of plant raw materials during their harvesting, processing and storage. The aim of the work is to analyze existing methods for determining color characteristics of plant raw materials described in foreign and domestic studies. Also, this paper presents the results of the experimental studies that describe the practical use of methods for measuring food product color. At present, the following methods for determining color characteristics by the sensor analysis principle are used: sensory, spectrophotometric and photometric. These methods have several disadvantages. Therefore, computer vision has found wide application as an automated method for food control. It is distinguished by high confidence and reliability in the process of determining freshness, safety, a degree of maturity and other parameters of plant raw materials that are heterogeneous in terms of the abovementioned indicators. The computer vision method is realized in the following systems: conventional, hyperspectral and multispectral. Each subsequent system is a component of the preceding one. Materials presented in the paper allow making a conclusion about the effectiveness of the computer vision systems with the aim of automatic sorting and determining quality of plant raw materials in the food industry.Качество пищевых продуктов обусловливает совокупность свойств продукции, таких как размер, форма, текстура, цвет и другие, и определяет приемлемость данной продукции для потребителя. По цвету можно определить дефекты в растительном сырье и классифицировать его по цветовым характеристикам, текстуре, размеру, форме, степени зрелости и т. д. В настоящее время ведутся работы по модернизации систем контроля цвета для быстрого и объективного измерения информации о цвете растительного сырья во время сбора, переработки, а также в процессе его хранения. Целью работы является проведение анализа существующих способов определения цветовых характеристик растительного сырья — они описаны в зарубежных и отечественных работах. Также в данной статье приводятся результаты экспериментальных работ, в которых рассказывается о практическом применении методов определения цвета пищевых продуктов. На сегодняшний день существуют следующие способы определения цветовых характеристик по принципу сенсорного анализа: органолептический, спектрофотометрический, фотометрический. Данные методы отличаются некоторыми недостатками, поэтому в качестве автоматизированного способа контроля пищевых продуктов широкое применение нашло компьютерное зрение. Он отличается высокой достоверностью и надежностью в процессе определения свежести, безопасности, степени зрелости и других параметров растительного сырья, отличающегося неоднородностью по перечисленным выше показателям. Метод компьютерного зрения находит свою реализацию в следующих системах: традиционной, гиперспектральной и многоспектральной. Каждая последующая система является составной частью предыдущей. Представленные в статье материалы позволяют сделать вывод об эффективности систем компьютерного зрения с целью автоматической сортировки и определения качества растительного сырья в пищевой промышленности

Tomato Flower Detection and Three-Dimensional Mapping for Precision Pollination

It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less environmental control. In response to this crisis, this research aims to act as a vital first step towards the development of a more robust autonomous pollination system for agricultural crop production. The main objective of this research is to develop a flower detection and mapping system for a field crop setting. This research presents a method to detect and localize tomato flowers within a three-dimensional (3D) region. Tomato plants were grown in a raised-bed garden where images were collected of the overhead view of the plants. Images were then stitched together using a photogrammetry technique, accomplished by the Pix4Dmapper software, to form an orthomosaic and 3D representation of the raised-bed garden from a high spatial resolution aerial view. Various machine learning architectures were trained to detect tomato flowers from overhead images and were then tested on the orthomosaic images produced by the Pix4D software. The coordinates of the detected flowers in the orthomosaic were then compared to the 3D model representation to find approximate 3D coordinates for each of the flowers relative to a predefined origin. This research serves as a first step in autonomous pollination by presenting a way for machine vision and machine learning to be used to identify the presence and location of flowers on tomato crops. Future work will aim to expand flower detection to other crops varieties in varying field conditions

An investigation of change in drone practices in broadacre farming environments

The application of drones in broadacre farming is influenced by novel and emergent factors. Drone technology is subject to legal, financial, social, and technical constraints that affect the Agri-tech sector. This research showed that emerging improvements to drone technology influence the analysis of precision data resulting in disparate and asymmetrically flawed Ag-tech outputs. The novelty of this thesis is that it examines the changes in drone technology through the lens of entropic decay. It considers the planning and controlling of an organisation’s resources to minimise harmful effects through systems change. The rapid advances in drone technology have outpaced the systematic approaches that precision agriculture insists is the backbone of reliable ongoing decision-making. Different models and brands take data from different heights, at different times of the day, and with flight of differing velocities. Drone data is in a state of decay, no longer equally comparable to past years’ harvest and crop data and are now mixed into a blended environment of brand-specific variations in height, image resolution, air speed, and optics. This thesis investigates the problem of the rapid emergence of image-capture technology in drones and the corresponding shift away from the established measurements and comparisons used in precision agriculture. New capabilities are applied in an ad hoc manner as different features are rushed to market. At the same time existing practices are subtly changed to suit individual technology capability. The result is a loose collection of technically superior drone imagery, with a corresponding mismatch of year-to-year agricultural data. The challenge is to understand and identify the difference between uniformly accepted technological advance, and market-driven changes that demonstrate entropic decay. The goal of this research is to identify best practice approaches for UAV deployment for broadacre farming. This study investigated the benefits of a range of characteristics to optimise data collection technologies. It identified widespread discrepancies demonstrating broadening decay on precision agriculture and productivity. The pace of drone development is so rapidly different from mainstream agricultural practices that the once reliable reliance upon yearly crop data no longer shares statistically comparable metrics. Whilst farmers have relied upon decades of satellite data that has used the same optics, time of day and flight paths for many years, the innovations that drive increasingly smarter drone technologies are also highly problematic since they render each successive past year’s crop metrics as outdated in terms of sophistication, detail, and accuracy. In five years, the standardised height for recording crop data has changed four times. New innovations, coupled with new rules and regulations have altered the once reliable practice of recording crop data. In addition, the cost of entry in adopting new drone technology is sufficiently varied that agriculturalists are acquiring multiple versions of different drone UAVs with variable camera and sensor settings, and vastly different approaches in terms of flight records, data management, and recorded indices. Without addressing this problem, the true benefits of optimization through machine learning are prevented from improving harvest outcomes for broadacre farming. The key findings of this research reveal a complex, constantly morphing environment that is seeking to build digital trust and reliability in an evolving global market in the face of rapidly changing technology, regulations, standards, networks, and knowledge. The once reliable discipline of precision agriculture is now a fractured melting pot of “first to market” innovations and highly competitive sellers. The future of drone technology is destined for further uncertainty as it struggles to establish a level of maturity that can return broadacre farming to consistent global outcomes

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Research and Creative Activity, July 1, 2019-June 30, 2020: Major Sponsored Programs and Faculty Accomplishments in Research and Creative Activity, University of Nebraska-Lincoln

Foreword by Bob Wilhelm, Vice Chancellor for Research and Economic Development: This booklet highlights successes in research, scholarship and creative activity by University of Nebraska–Lincoln faculty during the fiscal year running July 1, 2019, to June 30, 2020. It lists investigators, project titles and funding sources on major grants and sponsored awards received during the year; fellowships and other recognitions and honors bestowed on our faculty; books published by faculty; performances, exhibitions and other creative activity; and patents and licensing agreements issued. Based on your feedback, the Office of Research and Economic Development expanded this publication to include peer-reviewed journal articles and conference presentations and recognize students and faculty mentors participating in the Undergraduate Creative Activities and Research Experience Program (UCARE) and the First-Year Research Experiences program (FYRE). While metrics cannot convey the full story of our work, they are tangible measures of impact. Nebraska achieved a record $317 million in total research expenditures in FY 2019, a 26$450 million in research expenditures by 2025. Husker researchers are stimulating economic growth through university-sponsored industry activity. Nebraska Innovation Campus created 1,657 jobs statewide and had a total economic impact of $324.1 million in FY 2019. NUtech Ventures brought in$6.6 million in licensing income in FY 2020. The University of Nebraska system now ranks 65th among the top 100 academic institutions receiving U.S. patents, jumping 14 spots from 2019. I am proud of the Nebraska Research community for facing the challenges of 2020 with grit and determination. Our researchers quickly adapted to develop solutions for an evolving pandemic — all while working apart and keeping themselves and their families safe. As an institution, we made a commitment to embrace an anti-racism journey and work toward racial equity. Advancing conversations and developing lasting solutions is among the most important work we can do as scholars. Against the backdrop of the pandemic, rising racial and social tensions, and natural disasters, Nebraska researchers worked diligently to address other pressing issues, such as obesity and related diseases, nanomaterials, agricultural resilience and the state’s STEM workforce. Let’s continue looking forward to what we can accomplish together. Thank you for participating in the grand challenges process and helping identify the wicked problems that Nebraska has unique expertise to solve. Soon, ORED will unveil a Research Roadmap that outlines how our campus will develop research expertise; enrich creative activity; bolster commitment to diversity, equity and inclusion; enhance economic development; and much more. Amidst the uncertainty of 2020, I remain confident in our faculty’s talent and commitment. I am pleased to present this record of accomplishments. Contents Awards of $5 Million or More Awards of$1 Million to $4,999,999 Awards of$250,000 to $999,999 Early Career Awards Arts and Humanities Awards of$250,000 or More Arts and Humanities Awards of $50,000 to$249,999 Arts and Humanities Awards of $5,000 to$49,999 Patents License Agreements Creative Activity Books Recognitions and Honors Journal Articles Conference Presentations UCARE and FYRE Projects Glossar