App-Based solution to identify & solve disease in plants crops
Abstract : The technology based modern agriculture industries are today’s requirement in every part of agriculture in Bangladesh. In this technology, the disease of plants is precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes the farmer doesn’t know what type of disease on the plant and which type of medicine provide them to avoid diseases. This research developed for crops diseases detection and to provide solutions by using image processing techniques. We have used Android Studio to develop the system. The crops diseases detection and solution system is compared the image of affected crops with database of CDDASS (Crops Diseases Detection and Solution system). If CDDASS detect any disease symptom, then provide suggestion so that farmers can take proper decision to provide medicine to the affected crops. The application has developed with user friendly features so that farmers can use it easily.
? The existing research lacks an accurate and fast detector of apple disease for ensuring the healthy development of the apple industry.
? If the image is found to be diseased, some existing works have further classified it into a number of diseases.
? The crops need to be monitored against diseases from the very first stage of their life-cycle to the time they are ready to be harvested. Initially, the method used to monitor the plants from diseases was the traditional naked eye observation that is a time-consuming technique which requires experts to manually monitor the crop fields.
? The expert systems are intelligent computer programs that are capable of offering solutions or advices related to specific problems in given domain, both in a way and at a level comparable to that of human expert in a field.
? Depending on the application, many of those problems may be solved or at least reduced by the use of image processing.
? To overcome this impact, we had an idea of having a mixed variety of images during the training phase (heterogeneity).
• The accuracy of Real-time detection of apple leaf disease using deep learning approach based on improved convolution neural networks is less when compared to the proposed system because it detects multiple diseases in a single system.
• An application built for the identification of disease affected plants and healthy plants is done and this proposed work is focuses on the accuracy values during the real field conditions, and this work is implemented by having several plant disease images.
• Proposed system opted to develop an Android application that detects plant diseases.
? One of the advantages of using Electronic expert systems is its ability to reduce the information that human users need to process, reduce personnel costs and increase throughput. Another advantage of expert system is that it performs tasks more consistently than human experts.
? It is important to note that the modularity of the method allows the use of the BRISK detector in combination with any other key point descriptor and vice versa, optimizing for the desired performance and the task at hand.
? This idea has been demonstrated in to be very efficient, however here we employ it in a far more qualitative manner.
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