In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception-v3’ network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.
Natural products derived from plants are emerging as potent biorational alternatives to synthetic insecticides for the integrated management of post harvest insects of maize. In this paper, effectiveness of botanicals including plant extracts, essential oils, their isolated pure compounds, plant based nano formulations and their mode of action against storage insects have been reviewed with special reference to maize. Plant based insecticides found to be the most promising means of controlling storage insects of maize in an eco friendly and sustainable manner. This article also throws light on the commercialization of botanicals, their limitations, challenges and future trends of storage insect management.
The parasitoid Coccygidium transcaspicum (Kokujev) (Hymenoptera: Braconidae: Agathidinae) was reared from fall armyworm or Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) in maize fields in South India (Telangana) during 2019. It is the first report of a host for C. transcaspicum and the first report of C. transcaspicum as a parasitoid of S. frugiperda across the globe. The present study contains the first report from India and the Oriental region, provides morphological identification details of C. transcaspicum and comparison notes from its closely allied species C. melleum (Roman) which is basically an Afrotropical species.
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