By: Aayushi Srivastav, Janhavi Tripathi, and Neha Tomar
1- Student, Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
2- Student, Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
3- Student, Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers’ livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance disease detection in crops. Diseases of plants have a major impact on agricultural productivity and profitability, endangering our global food security. Minimizing output losses and managing vegetation properly depend on the quick and exact detection of diseased crops. Most disease indicators depend on inspecting by hand, which is frequently time-intensive, laborious, and false. By automate the illness identification process and enhancing accuracy through precise image analysis, machine learning (ML) techniques—in particular, deep learning (DL) approaches—offer an acceptable alternative. Using both deep learning and machine learning techniques, this paper offers a thorough analysis of recent advancements in the detection of plant diseases. We cover various approaches, datasets, preprocessing techniques, and constraints addressed. We investigate different datasets, preprocessing techniques, algorithms, and difficulties encountered in practical implementations. Trends such as multicrop disease detection systems, real-time monitoring, and explained artificial intelligence (XAI) are also covered in the text. The findings we report demonstrate the promise of utilizing machine learning (ML) techniques to agriculture, showing the urgent need of reliable datasets, strong models, and answers that are easy for farmer to execute.
Keywords: Multicrop identification, agriculture, explainable artificial intelligence (XAI), artificial intelligence, deep learning, image analysis, plant disease detection, and continuous monitoring.
Citation:
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