Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.     

Value stream mapping approach and analytical network process to identify and prioritize production system’s Mudas (case study: natural fibre clothing manufacturing company)

The current research has been done to identify and prioritize the Mudas of the production line of a natural fibre clothing manufacturing company; in this way, value stream mapping has been used to identify the Mudas, then analytical hierarchy method has been used to screen the Mudas. In the first step, necessary information to design value stream of production process has been obtained by observation, measurement and interview; afterward, according to the obtained information and by forming a focus group, Mudas in the production line have been identified and prioritized according to seven categories of Mudas. In order to prioritizing the Mudas by the usage of analytical hierarchy method, the Super Decision software has been applied. Then, according to the obtained priorities, suggested map of the future value system has been drawn and some suggestions have been presented to eliminate Mudas and move towards the lean manufacturing.     

Biodegradability Study of the Biosurfactant Contained in a Crude Extract from Corn Steep Water

Over the last few years, the global biosurfactant market has raised due to the increasing awareness among consumers, for the use of biological or bio-based products. Because of their composition, it can be speculated that these are more biocompatible and more biodegradable than their chemical homologous. However, at the moment, no studies exist in the literature about the biodegradability of biosurfactants. In this work, a biosurfactant contained in a crude extract, obtained from a corn wet-milling industry stream that ferments spontaneously in the presence of lactic acid bacteria, was subjected to a biodegradation study, without addition of external microbial biomass, under different conditions of temperature (5–45 °C), biodegradation time (15–55 days), and pH (5–7). For that, a Box–Behnken factorial design was applied, which allowed to predict the percentage of biodegradation for the biosurfactant contained in the crude extract, between the range of the independent variables selected in the study, obtaining biodegradation values between 3 and 80%. The percentage of biodegradation for the biosurfactant was calculated based on the increase in the surface tension of samples of the crude extract. Furthermore, it was also possible to predict the variation in t_1/2 for the biosurfactant (time to achieve the 50% of biodegradation) under different conditions.     

Moving beyond ‘more crop per drop’: insights from two decades of research on agricultural water productivity

ABSTRACT

Concern over increasing water scarcity has led to the introduction of the concept of agricultural water productivity and an emphasis on interventions to achieve ‘more crop per drop’. Yet, a strong debate continues on how the concept is to be defined and used. Drawing largely from the irrigation literature, the origins of the concept and its methodological developments are reviewed, and its use in applied work over two decades is discussed. Based on this analysis of conceptual and applied research, key insights into the concept’s contributions and limitations are presented, as well as opportunities for further refinements.     

基于虚拟导航线的农业机器人精确视觉导航方法

针对农田、野外环境中无人工标记情况下的导航问题，提出了一种基于虚拟导航线的农业机器人精确视觉导航方法。该方法不需要铺设导航线或者路标即可引导机器人行走直线。首先，根据需求确定需要跟踪的目标区域，之后控制机器人调整方向直到目标移至视野中央；其次，根据机器人和目标的位置确定参照目标，并依据两个目标的位置确定虚拟导航线；然后，动态更新导航线，并结合虚拟定标线和虚拟导航线确定偏移角度和偏移距离；最后，利用偏移参数构建模糊控制表，并以此实现对机器人旋转角度和行走速度的调整。实验结果表明，该算法能较为精确地实现对导航路线的识别，进而利用模糊控制策略使机器人沿直线向目标行走，且导航精度在10 cm以内。     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.     

Worldwide aflatoxin contamination of agricultural products and foods: From occurrence to control

Aflatoxins represent a global public health and economic concern as they are responsible for significant adverse health and economic issues affecting consumers and farmers worldwide. Produced by fungal species from the Aspergillus genus, aflatoxins are a toxic, mutagenic, and carcinogenic group of fungal metabolites that routinely contaminate food and agricultural products. Climate and diet are essential factors in the aflatoxin contamination of food and subsequent human exposure process. Countri es with warmer climates and staple foods that are aflatoxin-susceptible shoulder a substantial portion of the global aflatoxins burden. Enactment of regulations, prevention of pre- and postharvest contamination, decontamination, and detoxification have been used to prevent human dietary exposure to aflatoxin. Exploiting their chemical and structural properties, means are devised to detect and quantify aflatoxin presence in foods. Herein, recent developments in several important aspects impacting aflatoxin contamination of the food supply, including: fungal producers of the toxin, occurrence in food, worldwide regulations, detection methods, preventive strategies, and removal and degradation methods were reviewed and presented. In conclusion, aflatoxin continues to be a major food safety problem, especially in developing countries where regulatory limits do not exist or are not adequately enforced. Finally, knowledge gaps and current challenges in each discussed aspect were identified, and new solutions were proposed.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Preparative Protein Crystallization

Preparative protein crystallization can possibly replace one or more chromatographic steps in downstream processing. The development of such crystallization processes is demanding: first, promising principal crystallization conditions must be identified; second, details about the process must be defined; and third, the crystals have to be separated from the mother liquor without putting harm of their crystalline integrity. State‐of‐the‐art about these three steps is developing fast by (i) employing new screening methods which are based on fundamental understanding of the interaction of the protein molecules, (ii) application of existing concepts of technical bulk crystallization of small molecules to preparative protein crystallization, and (iii) making available specific gentle separation machinery.     

食用农产品中产志贺毒素大肠埃希菌污染与家庭厨房食物安全现状分析及防控措施

本文通过对食用农产品中产志贺毒素大肠埃希菌(STEC)来源与传播途径分析,阐述了STEC污染与家庭厨房食物安全之间的关系,并对当前世界各国食品中STEC的监管情况进行阐述,从而提出我国控制食用农产品中STEC进入家庭厨房的解决方案。