Improving Precision Agriculture by Utilizing Resnet152 for Cassava Plant Disease Detection and Enhanced Crop Health Monitoring

Abstract

Manihot esculenta, a crucial crop, provides millions of people in many parts of the world with their primary source of food. However, the presence of plant diseases, which can considerably limit yields and result in financial losses for farmers, poses a serious challenge to the sustainable cultivation of cassava. Identification of these illnesses as soon as feasible is crucial in order to take prompt mitigation steps and stop extensive out breaks. In this paper, a brand-new method for detecting diseases in cassava plants is proposed. The aim of the study is to create a powerful computer vision model that can automatically recognize and categorize several illnesses affecting cassava plants from photographs of their leaves. Resnet-152, a deep learning model, was utilized to effectively identify and understand patterns related to various diseases improving crop health monitoring and cassava plant disease detection using ResNet-152 is a promising approach in modern agriculture. ResNet-152 is a deep learning model known for its exceptional performance in image recognition tasks. By leveraging this powerful neural network, we can enhance the accuracy and efficiency of cassava disease detection. ResNet-152 is a convolutional neural network that can effectively classify and detect various diseases in cassava plants based on input images, helping farmers identify and mitigate infections to protect their crops this offers numerous benefits to farmers and agricultural experts, as it enables them to monitor crop health on a large scale and detect diseases at an early stage.