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EAI/Springer Innovations in Communication and Computing

A Hybrid Application of Quantum Computing Methodologies to AI Techniques for Paddy Crop Leaf Disease Identification

A. Prema Kirubakaran, J. Midhunchakkaravarthy

Published Oct 3, 2023• 4 citations
Publisher
DOI
10.1007/978-3-031-35751-0_4

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Malnutrition is a concern in the current situation. This occurs due to issues in food security, which should be concentrated on to eradicate poverty. Though many techniques are evolving deep learning and machine learning that help the agriculture process to get a better yield, quantum computing is a trial run to test its achievability in increasing crop production. It is believed that quantum computing will help to resolve world hunger. The whirling qubits will help solve the problems in the crop production world. Can quantum computing end the difficulties faced in crop yield? if so, how? The other reason for writing this article is the urgency to implement quantum computing in agriculture. Quantum computing is the data processing of the future. In the world of agriculture, artificial intelligence with image processing is quite important. With colossal speedups and growth in the power savings of quantum computers and as the other research field is moving and focusing on the implementation of quantum computing, the idea of shifting quantum technique toward agriculture emerged. The qubit representation of bits in quantum computers as "0" and "1" simultaneously makes it different from classical computers where the bits are represented either as "0" or "1.” The conversion of qubits to bits will be applied through the qubit conversion method, to have more valid bit spacing for real-time evaluation and storing purposes.

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The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops.
The need for accuracy and scalability is imperative when it comes to monitoring soil health and detecting plant diseases using precision agriculture. This paper suggests a Quantum AI-based hybrid analytics approach that combines classical machine learning algorithms, Variational Quantum Circuits, and statistics modeling in order to provide improved agricultural decision making capabilities. For this purpose, we used the multi-modal datasets that include Soil Health Data set (Kaggle dataset, n=12,500) that contains soil pH, moisture, Nitrogen (N), Phosphorus (P), Potassium (K) information and PlantVillage dataset (54,306 leaf images from 38 classes). This paper proposes a hybrid framework of QSVM and QCNN to predict soil health status and detect crop diseases in a dataset. This research work has been experimentally evaluated with Qiskit simulator (8 qubits) and classical ML algorithms like Random Forest and CNN. Based on experimental results, it is clear that the proposed hybrid framework yields soil health prediction accuracy of 94.7%, which is better than classical ML approaches (88.3%). Further, the accuracy achieved by the QCNN framework for predicting crop disease is 97.2%, with precision, recall, and F1-score being 0.96, 0.97, and 0.965, respectively. It is observed that the computation time is reduced by around 21.4% in feature selection tasks. From the above discussion, it can be concluded that this paper explores the capability of Quantum Artificial Intelligence (AI) to provide timely assistance for precision farming applications.
Malnutrition is a concern in the current situation. This occurs due to issues in food security, which should be concentrated on to eradicate poverty. Though many techniques are evolving deep learning and machine learning that help the agriculture process to get a better yield, quantum computing is a trial run to test its achievability in increasing crop production. It is believed that quantum computing will help to resolve world hunger. The whirling qubits will help solve the problems in the crop production world. Can quantum computing end the difficulties faced in crop yield? if so, how? The other reason for writing this article is the urgency to implement quantum computing in agriculture. Quantum computing is the data processing of the future. In the world of agriculture, artificial intelligence with image processing is quite important. With colossal speedups and growth in the power savings of quantum computers and as the other research field is moving and focusing on the implementation of quantum computing, the idea of shifting quantum technique toward agriculture emerged. The qubit representation of bits in quantum computers as "0" and "1" simultaneously makes it different from classical computers where the bits are represented either as "0" or "1.” The conversion of qubits to bits will be applied through the qubit conversion method, to have more valid bit spacing for real-time evaluation and storing purposes.