2022· International Journal of Future Computer and Communication· 0 citations· 22 references
TL;DR
An artificial intelligence recognition model based on multi-scale convolutional neural network called LH-DenseNet is presented, which can enhance the potential of deep learning in the field of agriculture, allowing more autonomic and systematic systems emerge.
Abstract
—Agricultural production has become a key factor for the well-being of citizens and stability of the world economy. However, pest diseases in the fields will reduce the production of crops and pose a threat to food security. In order to solve this problem, farmers have to use more advanced pest recognition systems to identify the types of pest diseases quickly rather than making judgement based on their own observation. This paper presents an artificial intelligence recognition model based on multi-scale convolutional neural network called LH-DenseNet. In this model, the advanced deep convolution network model DenseNet is used as the basic framework, and the large-scale public dataset ImageNet is used to train the convolution neural network's powerful feature extraction ability by applying various data enhancement strategies. Extracting and integrating the global and detailed features of images can improve the accuracy of pest classification. Thus, it can enhance the potential of deep learning in the field of agriculture, allowing more autonomic and systematic systems emerge.
The stable production of crops such as corn, wheat, soybeans, and canola is increasingly threatened by widespread pest infestations. Conventional manual pest surveys are hampered by low operational efficiency, subjective assessment bias, and delayed feedback, thereby impeding their ability to satisfy the demands of precision agriculture. To address these challenges, we proposes an intelligent pest detection framework based on EfficientNet and Feature Pyramid Network (FPN) for fast and accurate field pest identification. EfficientNet is adopted as the lightweight attention-embedded backbone to extract hierarchical features, and multi-scale detection plus hierarchical FPN fusion are integrated to improve recognition performance for tiny, inconspicuous pests. The experimental results on 37 common pest species in field crops showed that the proposed model achieves a mean average precision at Intersection-over-Union (IoU) threshold 0.5 (mAP@0.5) of 98.89%, 1.57% average recognition error rate, and with an average inference time of merely 0.048 s per image, balancing outstanding detection accuracy and real-time performance. Furthermore, this approach delivers a lightweight, reliable, and automated monitoring solution for field pest surveillance, thereby facilitating data-driven, precise pest management and advancing the practice of sustainable, green precision agriculture.
He Zhang, Xiao-Chen Liu, Chenguang Wang et al.· Agronomy· 0 citations
The convergence of precision agriculture and artificial intelligence (AI) has revolutionized the monitoring and management of crop health, offering transformative solutions to the perennial challenge of pest and disease outbreaks. Traditional scouting methods, characterized by their time-intensive nature and susceptibility to human error, are increasingly being superseded by AI-driven systems capable of real-time, large-scale detection. This paper explores the architecture and application of AI-based pest and disease detection systems, emphasizing the integration of computer vision, deep learning, and remote sensing technologies. By leveraging Convolutional Neural Networks (CNNs) and high-resolution imaging from drones and ground-based sensors, these systems facilitate the early identification of biotic stresses, enabling precision interventions that drastically reduce pesticide usage and operational costs. The analysis delves into the technical frameworks of data acquisition, image processing, and predictive modeling, while addressing the critical challenges of data labeling, model generalization, and real-time processing requirements. Furthermore, it highlights the transition from manual, reactive management to proactive, data-driven agricultural systems. Ultimately, AI-based detection is positioned as a pivotal component of sustainable, high-productivity agriculture, essential for mitigating food insecurity and enhancing climate resilience through optimized resource application and ecologically conscious crop protection strategies.
Research Author· American Journal of Advanced...· 0 citations
Agriculture continues to be one of the principal contributors to the economy and food security of developing nations, yet farmers regularly face difficulties such as unpredictable weather, variable soil conditions, and crop diseases that reduce quality and income. This paper presents a Crop Prediction and Plant Disease Detection System that integrates machine learning and deep learning techniques within a single web-based platform. The system accepts agricultural parameters — nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall — and applies a Random Forest regression model to estimate the expected crop. In parallel, it allows farmers to upload images of crop leaves, which are pre-processed and classified by a Convolutional Neural Network (CNN) to identify plant diseases and recommend suitable treatment. The application is built using ASP.NET Core for the user-facing interface, authentication, and dashboard, while a Python-based REST API hosts the machine learning and deep learning models; Microsoft SQL Server is used for persistent storage of user data, predictions, and disease records. The proposed system combines two traditionally separate functions — crop estimation and disease diagnosis — together with treatment recommendations and prediction history, into a single decision-support tool for precision agriculture, and was validated through unit, integration, and system-level testing
Preetham B, Madhura B, Dr. Manjunath B· International Journal of Adv...· 0 citations
Agriculture is a key sector in developing economies, but crop diseases significantly impact productivity, food security, and farmers’ livelihoods. Early detection is crucial to minimize losses, yet traditional methods are slow, error-prone, and depend heavily on human expertise. Recent advancements in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled more efficient automated crop disease detection. This study reviews pre-2018 AI-based approaches, focusing on techniques such as image processing, feature extraction, and classification methods. It highlights models like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid systems combining traditional and modern techniques. The proposed approach includes preprocessing, segmentation, color transformation, and extraction of texture, color, and shape features, followed by supervised learning for classification. AI systems can detect subtle disease symptoms early, achieving over 90% accuracy under controlled conditions. Integration with mobile and IoT technologies enables real-time monitoring and decision support for farmers. However, challenges such as limited datasets, environmental variability, and computational constraints remain. Future work should focus on developing scalable, robust, and field-deployable solutions for diverse agricultural conditions.
J. Rogers, Brandon Truaxe· International Journal of Mod...· 0 citations
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 0 citations
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
K. Maithili· 0 citations
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