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ISSN: 2589-7217
CN: 10-1795/S
p-ISSN: 2097-2113

Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides

Agriculture plays a significant role in the economic sector. The automation in agriculture is the main concern and the emerging subject across the world. The population is increasing tremendously and...

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Review of agricultural IoT technology

Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products,...

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Computer vision in smart agriculture and precision farming: Techniques and applications

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial...

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Automation and digitization of agriculture using artificial intelligence and internet of things

The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity. In most of the countries where the expansion...

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Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments

Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational...

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A comprehensive review on automation in agriculture using artificial intelligence

Agriculture automation is the main concern and emerging subject for every country. The world population is increasing at a very fast rate and with increase in population the need for food increases...

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Towards sustainable agriculture: Harnessing AI for global food security

The issue of food security continues to be a prominent global concern, affecting a significant number of individuals who experience the adverse effects of hunger and malnutrition. The finding of a solution...

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Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety....

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Deep learning based computer vision approaches for smart agricultural applications

The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core...

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A review of imaging techniques for plant disease detection

Agriculture is the basis of every economy worldwide. Crop production is one of the major factors affecting domestic market condition in any country. Agricultural production is also a major prerequisite...

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Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review

An e-nose or an e-tongue is a group of gas sensors or chemical sensors that simulate human nose or human tongue. Both e-nose and e-tongue have shown great promise and utility in improving assessments...

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Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG

In recent times, the use of artificial intelligence (AI) in agriculture has become the most important. The technology adoption in agriculture if creatively approached. Controlling on the diseased leaves...

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A comprehensive survey on weed and crop classification using machine learning and deep learning

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos. This technology plays a crucial role in facilitating...

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A review of external quality inspection for fruit grading using CNN models

This article reviews the state of the art of recent CNN models used for external quality inspection of fruits, considering parameters such as color, shape, size, and defects, used to categorize fruits...

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

Rice stands as a crucial staple food globally, with its enduring sustainability hinging on the prompt detection of rice leaf diseases. Hence, efficiently detecting diseases when they have already occurred...

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PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets

Aggressive behavior among piglets is considered a harmful social contact. Monitoring weaned piglets with intense aggressive behaviors is paramount for pig breeding management. This study introduced...

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How artificial intelligence uses to achieve the agriculture sustainability: Systematic review

The generation of food production that meets the rising demand for food and ecosystem security is a big challenge. With the development of Artificial Intelligence (AI) models, there is a growing need...

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Deep convolutional neural network models for weed detection in polyhouse grown bell peppers

Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery. Automatic identification and classification of weeds can play a vital role...

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Robotization of banana de-handing under multi-constraint scenarios: Challenges and future directions

Banana de-handing is an important part of banana post-harvesting operation. The traditional artificial de-handing model has problems such as labor intensity, inaccurate cutting, uneven cutting surface,...

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Comparison of CNN-based deep learning architectures for rice diseases classification

Although convolutional neural network (CNN) paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures, few studies have focused on the performance comparison...

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

In this study, we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal....

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Classifying early apple scab infections in multispectral imagery using convolutional neural networks

Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards....

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A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions

Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning...

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Machine learning in nutrient management: A review

In agriculture, precise fertilization and effective nutrient management are critical. Machine learning (ML) has recently been increasingly used to develop decision support tools for modern agricultural...

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Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of the most challenging problems in agriculture. If the diseases are not identified in...

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