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Automatic identification of plant diseases is critical for agricultural crop protection so as to enhance the crop yield. The recent advances in deep learning and image processing gives hope for the development of efficient algorithms to address this issue. In this manuscript, we make use of these schemes to develop a Light-Weight Convolutional Neural Network (LWCNN) for identifying diseases in the leaves and ears of pearl millets. Although many models exist in the literature, the total number of parameters employed by our model is far fewer, by an order of thousand as compared to many other light-weight networks such as MobileNet(v2), EfficientNet, NASNet etc. Hence our scheme can be employed and run directly on devices with much lesser compute power. It is noteworthy that despite using few parameters, the proposed model achieves an accuracy of 97.4% in detecting the existence of the downy mildew disease in pearl millets, and takes the least time for both training and testing as compared to other models. To eliminate most of the pre-processing steps and to make our system suitable for on-field detection, we explore three single stage object detectors namely SSD, YOLOv3 and RetinaNet which localize and classify multiple instances of healthy and diseased leaves and ears in the image. We present a comparative analysis of the models and our experiments indicate that SSD is most suitable outperforming the other two models by a significant margin. [ABSTRACT FROM AUTHOR]

Food security is a major problem faced today. With primitive storage facilities, especially in developing countries, it often leads to extensive losses. This work aims to develop algorithms based on vision data to assess the food quality and deploy them in food storage facilities to detect early signs of spoilage. This paper presents various segmentation techniques for finding spoilt food. Novel optimization techniques have been developed and implemented to improve K-means clustering and multilevel thresholding. A hybrid of moth flame optimization (MFO) and gravitational search algorithm (GSA) has been developed. Also, in another hybrid, particle swarm optimization (PSO) was also incorporated along with MFO and GSA. Both the hybrids performed better than the individual algorithms and the MFO–GSA–PSO hybrid performed better than the MFO–GSA hybrid on the benchmark functions. Segmented images using optimized K-means were used for feature extraction using local binary patterns (LBP). Multiclass support vector machine was used for classification which gave an accuracy of 81% for features from segmented images obtained using MFO–GSA hybrid and 83.33% for that using MFO–GSA–PSO hybrid. Results of simple linear iterative clustering superpixels for segmentation have also been discussed. The segmented clusters are then used to judge the rottenness of the food. Classification using LBP and Haralick features of the segmented image obtained using graphs over superpixels gave an accuracy of 81.7% and 78% respectively. [ABSTRACT FROM AUTHOR]

The endocycle is a variant cell cycle consisting of successive DNA synthesis and gap phases that yield highly polyploid cells. Although essential for metazoan development, relatively little is known about its control or physiologic role in mammals. Using lineage-specific cre mice we identified two opposing arms of the E2F program, one driven by canonical transcription activation (E2F1, E2F2 and E2F3) and the other by atypical repression (E2F7 and E2F8), that converge on the regulation of endocycles in vivo. Ablation of canonical activators in the two endocycling tissues of mammals, trophoblast giant cells in the placenta and hepatocytes in the liver, augmented genome ploidy, whereas ablation of atypical repressors diminished ploidy. These two antagonistic arms coordinate the expression of a unique G2/M transcriptional program that is critical for mitosis, karyokinesis and cytokinesis. These results provide in vivo evidence for a direct role of E2F family members in regulating non-traditional cell cycles in mammals. [ABSTRACT FROM AUTHOR]