In the present study, nano-scale Zero Valent Iron (NZVI) was synthesized in ethanol medium by the method of ferric iron reduction using sodium borohydride as a reducing agent under atmospheric conditions. The obtained iron nanoparticles are mainly in zero valent oxidation state and remain without significant oxidation for hours. A systematic characterization of NZVI was performed using XRD, SEM and TEM studies. The obtained iron nanoparticles consist of a zero valent core surrounding a rest oxide shell. The diameter of iron nanoparticles was predominantly within the range 20-110 nm. Refractory azo-dye compounds used in the textile industry are commonly detected in many industrial waste water. In this study the removal efficiency of three azo dyes, namely, methyl orange, sunset yellow and acid blue a, with laboratory synthesized NZVI particles in relation to the NZVI dosage, dye concentration and pH was determined. Increasing the dose of NZVI particles enhanced the decolonization of the dyes. The degradation decreased with increasing solution pH and concentration of dyes. These findings demonstrated the fast removal of azo dye compounds with NZVI and the advantage of the synthesized NZVI particles to treat azo dye contaminated wastewater.

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Leaf disease of plants causes great loss in productivity of crops. So proper take care of plants is mandatory. Plants can be affected by various diseases. So Early diagnosis of leaf disease is a good practice. Computer vision-based classification of leaf disease can be a great way in diagnosing diseases early. Early detection of diseases can lead to better treatment. Vision based technology can identify disease quickly. Though deep learning is trending and using vastly for recognition task, but it needs very large dataset and also consumes much time. This paper introduced a method to classify leaf diseases using Gist and LBP (Local Binary Pattern) feature. These manual feature extraction process need less time. Combination of gist and LBP features shows significant result in classification of leaf diseases. Gist is used as global feature and LBP as local feature. Gist can describe an image very well as a scene. LBP is robust to illumination changes and occlusions and computationally simple. Various diseases of different plants are considered in this study. Gist and LBP features from images are extracted separately. Images are pre-processed before feature extraction. Then both feature matrix is combined using concatenation method. Training and testing is done on different plants separately. Different machine learning model is applied on the feature vector. Result from different machine learning algorithms is also compared. SVM performs better in classifying plant's leaf dataset.

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