Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore, the proposed study presents a new framework for the recognition of fruits and vegetable diseases. This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such as You Only Look Once (YOLO)v2 and Open Exchange Neural (ONNX) model. The localization model is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model. The localized images passed as input to classify the different types of plant diseases. The classification model is constructed by ensembling the deep features learning, where features are extracted dimension of from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input, 01 ReLU, 01 Batch-normalization, 02 fully-connected. The proposed model classifies the plant input images into associated labels with approximately 95% prediction scores that are far better as compared to current published work in this domain.     

Role of catalytic protein and stabilising agents in the transformation of Ag ions to nanoparticles by Pseudomonas aeruginosa

Biological routes of synthesising metal nanoparticles (NPs) using microbes have been gaining much attention due to their low toxicity and eco‐friendly nature. Pseudomonas aeruginosa JP2 isolated from metal contaminated soil was evaluated towards extracellular synthesis of silver NPs (AgNPs). Cell‐free extract (24 h) of the bacterial isolate was reacted with AgNO₃ for 24 h in order to fabricate AgNPs. Preliminary observations were recorded in terms of colour change of the reaction mixture from yellow to greyish black. UV‐visible spectroscopy of the reaction mixture has shown a progressive increase in optical densities that correspond to peaks near 430 nm, depicting reduction of ionic silver (Ag⁺) to atomic silver (Ag⁰) thereby synthesising NPs. X‐ray diffraction spectra exhibited the 2θ values to be 38.4577° confirming the crystalline and spherical nature of NPs [9.6 − 26.7 (Ave. = 17.2 nm)]. Transmission electron microscopy finally confirmed the size of the particles varying from 5 to 60 nm. Moreover, rhamnolipids and proteins were identified as stabilising molecules for the AgNPs through Fourier transform‐infrared spectroscopy. Characterisation of bacterial crude and purified protein fractions confirmed the involvement of nitrate reductase (molecular weight 66 kDa and specific activity = 3.8 U/mg) in the Synthesis of AgNPs.Inspec keywords: microorganisms, silver, nanoparticles, enzymes, molecular biophysics, ultraviolet spectra, visible spectra, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, catalysis, biochemistry, nanobiotechnologyOther keywords: catalytic protein, stabilising agents, Pseudomonas aeruginosa, metal nanoparticles, UV–visible spectroscopy, optical densities, ionic silver, atomic silver, X‐ray diffraction spectra, transmission electron microscopy, nitrate reductase, rhamnolipids, Fourier transform‐infrared spectroscopy, Ag     

Fast neighborhood search for the single machine earliness–tardiness scheduling problem

This paper addresses the one machine scheduling problem in which n jobs have distinct due dates with earliness and tardiness costs. Fast neighborhoods are proposed for the problem. They are based on a block representation of the schedule. A timing operator is presented as well as swap and extract-and-reinsert neighborhoods. They are used in an iterated local search framework. Two types of perturbations are developed based, respectively, on random swaps and earliness and tardiness costs. Computational results show that very good solutions for instances with significantly more than 100 jobs can be derived in a few seconds.     

用于片上网络的网络接口设计

提出一种用于由双通道路由器组成的片上网络系统的网络接口。该网络接口符合AMBA总线协议,能根据IP核的通信请求,自动选取通信方式,向IP核隐藏通信细节,充分利用双通道路由器中控制包通道与数据包通道分离的特点,方便系统编程。使用SMIC0．13gm工艺综合后,该网络接151的面积仅为0．3mm^2。     

Estimation and validation of LAI using physical and semi‐empirical BRDF models

This letter deals with estimation of LAI for a wheat crop using physical and semi‐empirical BRDF models and IRS‐1D LISS‐III sensor data. NDVI was computed for both the models with LAI as a free parameter. The model‐computed NDVI was compared with corresponding atmospherically corrected LISS‐III NDVI. The estimation of LAI was carried out on the basis of a look‐up table approach and minimum root mean squared deviation between model computed and observed NDVI. The estimated LAI was validated against field measurements carried out during the months of February and March 2003, at the Central State Farm, Rajasthan, India. It was found that LAI was underestimated in both physical and semi‐empirical models. Results show that inclusion of multiple scattering in physical models may not always lead to a more accurate estimation of LAI and that it may be possible to estimate LAI at early stages of crop growth using semi‐empirical models. The coefficient of determination (R ²) between model estimated and measured LAI was 0.57 (standard error of estimate (SE) 0.156) and 0.63 (SE 0.187) for semi‐empirical and physical models, respectively, in the single scattering approximation, for February data. The corresponding values for March data were 0.57 (SE 0.206) and 0.51 (SE 0.216), respectively.     

Integrated batch sizing and scheduling on a single machine

In this paper, we address the integrated batch sizing and scheduling problem. We consider a single machine which can handle at most one customer order at a time and for which the nominal production rate is the same for all the customer orders. Demand is deterministic, and all the orders are ready to be processed at time zero and must be delivered at a given due date. Each order can be satisfied from different batches. Upper and lower bounds on the size of the batches are considered. We seek a feasible schedule that minimizes the sum of the tardiness costs and the setup costs incurred by creating a new batch. We present some structural properties of the optimal schedules for both single-order and multiple-order problems and then propose dynamic programming algorithms based on these properties. Computational results that show the efficiency of the method are reported.     

Finite Time Fractional-order Adaptive Backstepping Fault Tolerant Control of Robotic Manipulator

In this paper, fractional calculus theory is employed to inspect a finite time fault tolerant controller for robotic manipulators in the presence of uncertainties, unknown external load disturbances, and actuator faults, using fractional-order adaptive backstepping approach in order to achieve, fast response and high-precision tracking performance. Knowing the advantages of adaptive controllers an adaptive form of the above controller is then established to deal with the overall uncertainties in the system. The most important property of the proposed controller is that we do not need to have knowledge about the actuator fault, external disturbances and system uncertainties exist in system. In this study two important achievements are made. The first one is that the finite time convergence of closed-loop system is ensured irrespective of initial states values. The second one is that the effects of the actuator faults and other uncertainties are attenuated by the suggested controller. The performance of the suggested controller is then tested for a PUMA560 robot in which the first three joints are used. The simulation results validate the usefulness of the suggested finite-time fractional-order adaptive backstepping fault-tolerant (FOAB-FTC) controller in terms of accuracy of tracking, and convergent speed.

     

Argument mining based on a structured database and its usage in an intelligent tutoring environment

Argumentation theory is a new research area that concerns mainly with reaching a mutually acceptable conclusion using logical reasoning. Argumentation can be defined as a proof of dynamic nature and is considered as an ill-defined domain that typically lacks clear distinctions between “right” and “wrong” answers. Instead, there are often competing reasonable answers. Recently, a number of argument mapping tools have been developed to diagram, articulate, and comprehend different arguments. Despite the fact, these methods are of complementary nature, and the efforts for integrating these tools are missing. The purpose of this paper is threefold: (1) revealing a novel approach for argument representation using a structured relational argument database “RADB”, which has been designed, developed, and implemented in order to represent different argument analyses and diagrams, (2) presenting a classifier agent that utilizes the RADB repository by using different mining techniques in order to retrieve the most relevant arguments to the subject of search, and (3) proposing an agent-based educational environment (ALES) that utilizes the RABD together with the classifier agent to teach argument analysis.