An IoT Based Secure Smart Farming

Agrotechnology takes into account the soil and climatic conditions in order to guarantee a high crop production with the best possible labour investment. The progress of farming as a whole has been greatly influenced by advancements in plant physiology. Use of organic and mineral fertilizers has been rationalized. A scientific rationale for the necessity of irrigation has been developed via studies of the soil and plant water requirements. When the right conditions, such as the right temperature, humidity, sunlight, moisture, nutrients, etc. were present, these developments led to an increase in the soil's effective fertility, which in turn contributed to boost crop output. Crops are chosen based on the quality of the soil and the climate. The suggested agro method helps farmers identify the type of soil with the aid of sensors that can measure the soil's moisture content, pH level, and nutrient content (NPK). The farmer will be able to choose the right crops to plant with the aid of these values. Crop selection should be based on a number of parameters, including the kind of soil, its texture, and its nutrient content. The major goal is to increase plant health while simultaneously lowering excessive water use. It must guarantee that the crops are not over-or under-irrigated. Building a water supply system that will consider the soil's moisture content and disperse water as the soil requires can solve this issue, without harming the crop and without using excessive amounts of water. We can continuously check the amount of water in the soil by utilising inexpensive sensors. When the water level drops and crosses a certain threshold, the sensors will trigger a sprinkler or a pump that is connected to it, providing water to the crops. This arrangement will assist small-scale farmers in increasing crop yields, which will boost the Indian economy. The IoT module has sensors for soil moisture, temperature, pH, and NPK that collect data in real time and upload it to a cloud storage. Then, pre-trained models created using machine learning methods will be compared with the real-time sensed data. The Sensor Information Unit, which is connected to the secure cloud storage, receives data from the deployed sensors. A machine learning algorithm will be utilised to anticipate the appropriate crops that can be produced in that particular soil utilising the data obtained by the cloud for data analysis. The machine learning module will additionally predict when fertilisers will need to be applied and how precise monitoring will result in a high crop yield.