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.
The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world’s food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.
Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions.
India is a very large country where 69% of total population depends on Agriculture. Agriculture has a great role in Indian economy because it contributes significantly about 14.8% in 2013-14 to the Gross Domestic Product. The percentage share of import and export of plant commodities have been of the order 3.22% and 13.79% of national import and export in 2013-14. There has always been the possibility of moving exotic insect pests, diseases and weeds from other country to India. Exotic pests and diseases introduced into India cause a huge damage to Indian agricultural trade. For these reasons to prevent the introduction of various exotic pests, diseases and weeds from other countries or within states of country, legal restrictions are enforced which is commonly known as Plant Quarantine. Plant Quarantine regulations at national level are known as Domestic Quarantine as well as at international level known as Foreign Quarantine. The implementation of the quarantine measures is assisted by legal approval, called quarantine laws. It acts as an important technique or procedure to exclude exotic pests from the crop. Efficient implementation of quarantine is extremely emphasized to manage pests, consequently which helps in sustaining the productivity of crops. Now mobile phones are very much preferable by the common men. Information can easily be shared through the mobile application. At present Plant Quarantine import regulations are presented in pdf form hence it is a tedious process for the users to find their specific requirement. It is very hard for an importer to know at any situation about these regulations whether any plant commodity is prohibited or allowed to import in India. Thus the need arises to provide the information in searchable manner. Import regulations are enlisted in four schedules with justification, condition, additional declaration on the basis of Commodity, Country of origin of the commodity, Category of the Commodity, Plant Part of the Commodity. This paper presents the requirement analysis, design, development and testing of an android based mobile application with different features which provide information about import regulations related to Commodity, Country Of Origin, Category, Plant Part which is promulgated by the Directorate of Plant Protection, Quarantine & Storage, Department of Agriculture, Cooperation & Farmer Welfare, Ministry of Agriculture and Farmer Welfare, Government of India to prevent the entry and spread of dangerous pests and pathogens.
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