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

InstaCropNet: An efficient Unet-Based architecture for precise crop row detection in agricultural applications

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields. Among various navigation techniques, visual navigation using widely available RGB...

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Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus plants. However, as a training area, it lacks appeal and learning motivation due to its conventional...

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Enhanced detection algorithm for apple bruises using structured light imaging

Bruising reduces the edibility and marketability of fresh apples, inevitably causing economic losses for the apple industry. However, bruises lack obvious visual symptoms, which makes it challenging...

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Automated quality inspection of baby corn using image processing and deep learning

The food industry typically relies heavily on manual operations with high proficiency and skills. According to the quality inspection process, a baby corn with black marks or blemishes is considered...

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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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Real-time litchi detection in complex orchard environments: A portable, low-energy edge computing approach for enhanced automated harvesting

Litchi, a succulent and perishable fruit, presents a narrow annual harvest window of under two weeks. The advent of smart agriculture has driven the adoption of visually-guided, automated litchi harvesting...

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Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments

For the purpose of monitoring apple fruits effectively throughout the entire growth period in smart orchards. A lightweight model named YOLOv8n-ShuffleNetv2-Ghost-SE was proposed. The ShuffleNetv2 basic...

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Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds

The ability to automatically recognize sheep breeds holds significant value for the sheep industry. Sheep farmers often require breed identification to assess the commercial worth of their flocks. However,...

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Harvest optimization for sustainable agriculture: The case of tea harvest scheduling

To ensure sustainability in agriculture, many optimization problems need to be solved. An important one of them is harvest scheduling problem. In this study, the harvest scheduling problem for the tea...

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Machine learning-based spectral and spatial analysis of hyper- and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological...

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Crop diagnostic system: A robust disease detection and management system for leafy green crops grown in an aquaponics facility

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle, just like traditional agriculture. The early detection of diseases is crucial to witnessing...

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Machine learning for weed–plant discrimination in agriculture 5.0: An in-depth review

Agriculture 5.0 is an emerging concept where sensors, big data, Internet-of-Things (IoT), robots, and Artificial Intelligence (AI) are used for agricultural purposes. Different from Agriculture 4.0,...

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Cumulative unsupervised multi-domain adaptation for Holstein cattle re-identification

In dairy farming, ensuring the health of each cow and minimizing economic losses requires individual monitoring, achieved through cow Re-Identification (Re-ID). Computer vision-based Re-ID relies on...

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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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Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model

For commercial broiler production, about 20,000–30,000 birds are raised in each confined house, which has caused growing public concerns on animal welfare. Currently, daily evaluation of broiler wellbeing...

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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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Corn kernel classification from few training samples

This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages,...

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Low-cost livestock sorting information management system based on deep learning

Modern pig farming leaves much to be desired in terms of efficiency, as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight. This method is...

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CactiViT: Image-based smartphone application and transformer network for diagnosis of cactus cochineal

The cactus is a plant that grows in many rural areas, widely used as a hedge, and has multiple benefits through the manufacture of various cosmetics and other products. However, this crop has been suffering...

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Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables: State of the art and perspectives

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many fields, including agriculture. This paper aims to shed light on deep learning techniques used in agriculture for...

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Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration

Efficient irrigation management of urban landscapes is critical in arid/semi-arid environments and depends on the reliable estimation of reference evapotranspiration (ETo). However, the available measured...

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Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features. These techniques are also being integrated...

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Rice disease identification method based on improved CNN-BiGRU

In the field of precision agriculture, diagnosing rice diseases from images remains challenging due to high error rates, multiple influencing factors, and unstable conditions. While machine learning...

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Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls

Robotic harvesting of cotton bolls will incorporate the benefits of manual picking as well as mechanical harvesting. For robotic harvesting, in-field cotton segmentation with minimal errors is desirable...

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Leguminous seeds detection based on convolutional neural networks: Comparison of Faster R-CNN and YOLOv4 on a small custom dataset

This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and it can be very difficult to distinguish between them. Botanists and those who study plants,...

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