Applications for Detecting Plant Diseases Based on Artificial Intelligence

Abstract

Agriculture is an activity to manage biological natural resources with the help of technology and labor. The presence of diseases in plants that suddenly inhibit plant growth is alarming to farmers. So, farmers cannot determine what conditions these plants suffer. This study will discuss the implementation of Artificial Intelligence-based plant disease detection software. At this stage, deep learning models are created using cameras matched with objects. The application development is to detect diseases in plants. The fourth step is testing. This application includes the implementation of Convolutional Neural Network and Recurrent Neural Network, which provides Artificial Intelligence architecture to diagnose plant diseases, and offer solutions to those plants from the results of research with tomato plant sample tests obtained four categories of disease Early Blight disease with a prediction of 100%, Bacterial Spots 90%, Healthy 100%, Late Blight 100% a system that can recommend health care related to crops based on images so that it can help farmers identify types of plant diseases. This application can help farmers to reduce crop failure for farmers caused by plant diseases to improve the quality of agricultural and plantation products

Artificial Intelligence: A Promising Tool for Application in Phytopathology

“…The application in this study was tested on tomato plant diseases, achieving prediction accuracies of 100% for early blight, 90% for bacterial spot, and 100% for both healthy and late blight conditions. This research showcases the application's capability to recommend treatment options based on image analysis, offering a significant tool for farmers to identify and manage plant diseases effectively [122][123][124].…”

“…This innovative approach merges a mobile application with AI algorithms, providing real-time disease diagnostics and disease density mapping. This collaborative and technology-driven approach reflects a shift towards more integrated and interconnected systems for plant disease management, akin to the initiatives by Otero et al [122] and Zen et al [124]. The study by Zen et al focuses on developing an AI-based mobile application for detecting plant diseases with high accuracy, utilizing CNN and RNN with TensorFlow.js.…”

“…It is notably how neural networks, especially convolutional neural networks (CNNs), are emerging as the predominant technique for classifying plant diseases, thanks to their inherent flexibility and automatic feature extraction capabilities [124,127]. The developments shown in this subsection mark a milestone at the confluence of advanced technology and agronomy, heralding a new era in plant disease management.…”

Optimizing Multi-Level Crop Disease Identification using Advanced Neural Architecture Search in Deep Transfer Learning

“…Machine learning models examine factors such as temperature, humidity, and leaf colour using image recognition techniques to diagnose plant diseases [8]. Convolutional neural networks (CNNs), in particular, are deep learning models that are capable of classifying leaf pictures to differentiate between healthy and unhealthy plants, as well as identifying the crop species [9][10] [11]. We employ state-of-the-art deep learning models and advanced sensors to create an automated system that can accurately and quickly diagnose rust disease in Vicia faba L. [12].…”