Plant Disease Identification and Crop Recommendation Using Machine Learning And Deep Learning

Abstract: Identification of plant diseases and advice on crops are essential duties for agricultural businesses. The use of algorithms based on deep learning and machine learning has become crucial due to the rising demand for food supply and the need to maximize agricultural yields. In this article, we suggest a paradigm for the use of the machine and deep learning methods to identify plant illnesses and to prescribe crops and fertilizers. The framework consists of three main components: plant disease identification, crop recommendation, and fertilizer recommendation. In the plant disease identification component, we use the image classification technique to identify the type of disease affecting the plant based on its symptoms. We then use deep learning algorithms such as convolutional neural networks (CNNs) to classify the disease accurately. In the crop recommendation and fertilizer recommendation components, we use regression algorithms to predict the yield of crops based on various factors such as soil type, climate, and the presence of plant diseases. The results of our experiments demonstrate the efficiency of our framework in accurately identifying plant diseases and recommending crops with high yields. Our approach can help farmers make informed decisions about the crops they should grow, and the diseases they should be aware of, leading to better crop yields and reduced crop losses

Keywords: Agriculture,CNN,Regression.