Enhancing Crop Health Through Image Analysis of Leaf Diseases Prediction and Remedial Suggestion

Abstract

Introduction The spread of crop diseases has posed serious problems for the agricultural industry recently, as it can lead to significant yield losses and monetary difficulties for farmers. Early and accurate diagnosis is essential for the management and control of these diseases. This work introduces a deep learning method using convolutional neural networks (CNNs) for the detection and classification of various crop leaf illness. With the use of an extensive dataset that includes a range of leaf images impacted by distinct diseases, our CNN model is skilled in correctly identifying and categorizing illnesses. The suggested method offers a reliable, automated, and scalable crop disease monitoring solution, showcasing. The possibilities of deep learning methods in agricultural applications. According to experimental results, our model performs better than conventional image processing techniques, achieving high recall rates and precision for a variety of disease classes. This study highlights how incorporating artificial intelligence into agriculture can have a revolutionary effect and open the door to more intelligent and effective disease management techniques.

Future research could focus on expanding the dataset to include more crop types and rare diseases to improve model generalization. Additionally, the development of real-time, mobile-based applications that incorporate this technology could also be explored, enabling farmers to detect diseases on-site and take timely remedial suggestion to mitigate potential crop losses.