Abstract:

Agriculture is the primary method used to cultivate various plant species to produce food and a variety of other desired items as well as to raise domestic animals. Because there is no nutritional shortfall in plants, farmers are unable to identify which nutrient is declining. Early farmers used conventional ways to detect nutrient deficiencies in crop output, but it was difficult to do so, which posed a serious dilemma for farmers. This paper played a key part in the development of an automation that aids farmers by showing them the results of nutrient deficiency in plants with just one click of an image. Using a traditional neural network, the leaf's picture is processed (CNN). This method uses picture capture and CNN image processing. It will compare the image to the already existing set, show the actual result on the screen, and suggest how to correct the deficit using the fertilizers we foresee. It needs to be applied according to the percentage we've specified after measuring the amount of agricultural production harm that has been done to plants. It will assist contemporary farmers in recognize and easing their burden.

Description:

Nutrient deficiency, Detection, Prediction, Automation, CNN, Fertilizer