Plant Disease Detection and Fertilizer Recommendation using CNN

These days, text, music, video, and mixed media are used on digital platforms even more frequently. More people than ever before are exchanging information and insights. Using machine learning and deep learning has become essential in farming. These days, they're essential for diagnosing plant illnesses and making fertilizer recommendations. Advances in these fields allow us to better assess plant health and optimize fertilizer use, allowing us to grow more crops in a more sustainable manner. Deep learning has advanced significantly recently, particularly with deep neural networks. The accuracy with which we are able to identify plant issues and make fertilizer recommendations has been greatly enhanced by these networks. When analyzing data that is sequenced or varies over time, we frequently employ deep neural network types like Convolutional Neural Networks (CNN). When it comes to identifying plant illnesses, these smart networks are able to examine plant images to detect any indications of illness or pests. Furthermore, these models can also consider crop types, weather patterns, and soil characteristics when proposing fertilizers, allowing for highly accurate fertilizer recommendations. This article explores the applications of sequential deep neural networks in agriculture, particularly for plant disease detection and fertilizer recommendation. It examines the issues we encounter and several approaches that use CNNs in this subject.