In agriculture, timely disease control and management plays an important role in optimizing yield, improving harvest quality and minimizing loss to farmers. Automated disease management tools turn out to be of significant aid to farmers. In our paper, we present efficient automated disease management techniques in potato. Potato is world's fourth largest food crop, cultivated in many parts of the globe. Potato crops are majorly affected by fungus infections namely early blight and late blight in different stages of growth. These appear as brownish spots and black lesions on the leaves. The images of potato leaves are captured and analyzed to detect disease symptoms and further classified as diseased or normal. Here, we use image processing and machine learning techniques. The comparison of performance of classifiers support vector machine (SVM), random forest (RF) and artificial neural network (ANN) is performed on the same test dataset of potato leaves. From results, we observe ANN scores highest of 92 % accuracy followed by SVM with 84 % accuracy and RF with 79% accuracy.
Food is necessary for basic needs, farming is regarded as a global source of commerce. Horticulture is a significant source of employment in non-industrialized countries like India. The activities involved in farming are broadly categorized into three key areas: reaping and post-collection. AI is the present innovation helping agriculture people cultivate bits of knowledge about the pre-harvesting and post-harvesting approaches. This paper presents a broad study of most recent AI & ML application in agribusiness to mitigate the issues in pre collecting, reaping, and post gathering.
In today's internet era, with online transactions almost every second and terabytes of data being generated everyday on the internet, securing information is a challenge. Cryptography is an integral part of modern world information security making the virtual world a safer place. Cryptography is a process of making information unintelligible to an unauthorized person. Hence, providing confidentiality to genuine users. There are various cryptographic algorithms that can be used. Ideally, a user needs a cryptographic algorithm which is of low cost and high performance. However, in reality such algorithm which is a one stop solution does not exist. There are several algorithms with a cost performance trade off. For example, a banking application requires utmost security at high cost and a gaming application sending player pattern for analytics does not bother much about security but needs to be fast and cost effective. Thus, amongst the cryptographic algorithms existing, we choose an algorithm which best fits the user requirements. In, this process of choosing cryptographic algorithms, a study of strengths, weakness, cost and performance of each algorithm will provide valuable insights. In our paper, we have implemented and analyzed in detail cost and performance of popularly used cryptographic algorithms DES, 3DES, AES, RSA and blowfish to show an overall performance analysis, unlike only theoretical comparisons.
In the recent past, e-commerce sites have made rapid growth. There are thousands of products and various websites sell these products. Massive growth in the number of reviews and their availability along with the advent of opinion-rich review forums for the products sold online, choosing the right one from a large number of products has become difficult for the users. HELP-ME-BUY APP is an android application that assists buyers in online shopping. It is imminent for buyers to verify for genuineness and quality of products. What better way is there than to ask people who have already bought the product? This is when customer reviews come into picture. The major hitch here is popular products have thousands of reviews-we do not have the time or patience to read all thousands of them. Hence, our application eases this task by analyzing and summarizing all reviews which will help the user decide what other buyers have experienced on buying this product. We carry out this process by a number of modules that include feature extraction and opinion extraction which improves the process of analysis and helps in the formation of an efficient summary.
Our world has evolved to an optimal point of advancement. The extravagant growth has helped in the invention of technologies, industry standards, gadgets, and devices that produce enormous amount of data that all require an essential data management and manipulation system. The data acquired from the various input and output sources are indulged in providing a certain infrastructure are also susceptible to damages if not treated well which may result in loss of data. To overcome this loss, various strategies that run parallel to prevent such loss are being used, one such example is the NoSQL MongoDB. MongoDb is a cross-platform, document oriented database that provides, high performance and easy scalability ensuring effective data management with its prominent feature of auto sharding. Sharding splits the database across multiple servers, increasing the capacity and scalability as required. This feature handles distribution of data in different nodes to maximize disk space and dynamically load balance queries. Partitioning the databases appropriately is a major step that determines the efficiency of sharding. This involves choosing an index of the MongoDB, competently as a shared key for further horizontal scaling of the database. Our current research involves the study of this load balancer. This paper intends to ascertain the need for NoSQL databases in the present situation and emphasize advancement of document-oriented database - MongoDB in particular by describing with a quantitative example that SQL databases are prone to deterioration when data is over loaded and MongoDB comes with inbuilt load balancer which makes it a better solution in applications with high data load. We describe the technology of sharding - auto load balancing feature of MongoDB and hope to provide a comprehensive insight of the process.
Suitable soil water amount is an obligatory condition for ideal plant growth. Also, water being a crucial element for life nourishment, there is the prerequisite to circumvent its excessive use. Irrigation is a supreme consumer of water. This calls for the need to control water supply for irrigation purposes. Pasture should neither be over-irrigated nor under-irrigated. Soil Monitoring is one tool to provide soil information. Over time, systems have been applied so as to approach register this aim of which computerized procedure are the most accepted as they permit data to be gathered at high persistence with less work demand. Size of the current structure engage micro-processor based systems. These systems provide several technological supremacy but are high-priced, large, hard to sustain and less welcomed by the technologically untrained operators in the pastoral scheme. The objective of this project is to outline a manageable, facile to install technique to detect and specify the level of soil moisture that is endlessly managed with a view to attain pinnacle plant growth and concomitantly augment the obtainable irrigation resources. In this project we use the information obtained from the input sensors which is handled using the neural networks algorithm and correction factors for monitoring. Soil monitoring, providing a series of assessments showing how soil conditions and/or properties change over time. The use of simple obtainable components decreases the manufacturing and maintenance costs. This makes this system more economical, appropriate and a low maintenance solution for applications, mainly in rural areas and for small scale agriculturists.
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