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Crops have ever been a primary source of food for humans as it provides us energy to carry out our everyday tasks. Every person requires food for his survival. During the early ages, the food requirement was far less as the human population was very less and sparse but after the fourth industrial revolution population explosion occurred due to which there was a sharp increase in population, and as a result, the food demand also spiked due to which shortage of food had occurred which still exists. This shortage was primarily caused due to two reasons-Increase in Agriculture destruction and a sharp increase in population. Deep learning has brought a new era of introducing intelligence to our artificial devices to imitate a task like humans without being programmed with pre-defined rules to do so. In this paper, we propose to integrate Deep learning to reduce the loss of crops due to crop infections caused by various microbes. We implement an Android solution operating in a mobile environment that integrates the Deep Learning Neural Network and provides an on-device image recognition of crop diseases. The deep learning model acquires an accuracy of 95% and is a modified MobileNetV2 model which is converted to a Siamese Network. This model is deployed as an Android Application with high performance and a higher accuracy while only consuming the resource of that device. Due to all the factors, this solution can be widely implemented due to its higher accuracy as well as it is cost-friendly.

Traditional agriculture is converting into smart agriculture due to the bulge of the IoT (Internet of things). An agriculture services platform is developed to support environmental monitoring and to improve the efficiency of agriculture management. Contemporary the Internet of things (IoT) is one of the highest promising application areas in information technology for forthcoming products and services. And, the agriculture field is changing expeditiously pointing to the future of automated and embedded systems with a bunch of sensors to monitor and curb the flourishing plants in a way to profit associated with it. However, one of the major issues of IoT is still a conversion between devices, notably on long-range. LoRa is lately accepted as auspicious communication technology, due to its properties such as long-range, two-way communication, and low cost. In this work, the LoRa technology is applied in the agriculture sector for making long-distance, low-cost communication. This work includes sensing and monitoring the parameters such as level, temperature, and humidity and also controlling the said parameters with the help of a remote unit. The proposed system is based on a high-performance microcontroller, integrated temperature and humidity sensor on real-time data analysis. The use of Data collection, pattern classification and apply strategic analysis then control execution for the final result.

Humans communicate with one another in order to share their views, emotions, and stories with other people. For people suffering from communication disorders, it is not the case. Deaf-mute people can interact owing to sign language. The idea of this research is to build a system for identifying sign language that allows people with speech impairments and regular people to communicate, bridging the communication barrier. Furthermore, being able to recognize common sentences that are used frequently becomes crucial for conversations to flow effectively. Such a system is essential in services like banking where privacy would be a top priority.