Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Mamat, Normaisharah; Othman, Mohd Fauzi; Abdulghafor, Rawad; Belhaouari, Samir Brahim; Normahira, M.; Hussein, Shamsul Faisal Mohd

doi:10.3390/agriculture12071033

Cited by 23 publications

(10 citation statements)

References 188 publications

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“…By predicting the quality of rice and detecting pesticide residues, farmers and processors can take appropriate measures to reduce pesticide use, optimize cultivation and processing, and improve the market competitiveness of rice. Meanwhile, the application of deep learning provides important support and guidance for the rice industry and the quality control of agricultural products 25 , 26 . By predicting and assessing the quality of rice, agricultural departments can formulate appropriate policies and standards, strengthen supervision and management, and ensure that agricultural products meet quality and safety requirements.…”

Section: Resultsmentioning

confidence: 99%

Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer

Jiang,

Deng,

Dong

et al. 2024

Sci Rep

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Rice serves as a fundamental food staple for humans. Its production process, however, unavoidably exposes it to pesticides which may detrimentally impact its quality due to residues. Therefore, it is extremely necessary to monitor pesticide residues on rice during storage. In this research, the Quatformer model, which considers the effects of temperature and humidity on pesticide residues in rice grains, was utilized to forecast the amount of pesticide residues in rice grains during the storage process, and the predicted results were combined with actual observations to form a quality assessment index. By applying the K-Means algorithm, the quality of rice grains was graded and assessed. The findings indicated that the model had high prediction accuracy, and the MAE, MSE, MAPE, RMSE and SMAPE indexes were calculated to be 0.0112, 0.0814, 0.1057, 0.1055 and 0.0204, respectively. These findings provide valuable technical and theoretical support for planning storage conditions, enhancing pesticide residue decomposition, and monitoring rice quality during storage.

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Section: Resultsmentioning

confidence: 99%

Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer

Jiang,

Deng,

Dong

et al. 2024

Sci Rep

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“…The fast and accurate detection of crop types, along with positioning, is a prerequisite for the efficient and accurate operation of harvesting machineries [ 5 ]. The methods of crop detection and classification are mainly categorized into two types: traditional machine-vision-based approaches [ 6 ] and deep-learning-based approaches [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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“…They have a known grid-like topology that can be used in time series data (1-D grid) or image data (2-D grid), that take samples regular time intervals, or across pixels, respectively. For a recent comprehensive review of CNNs applied to fruit image processing, see [21,[31][32][33].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…The feature map extraction is computed, and the input is classified based on this feature map. We refer the reader to [2,31,34] for a comprehensive explanation of CNNs. Regions with convolutional neural networks (R-CNN) are used to find and classify diseases and defects in raspberries, called objects, in a raspberry tray image in the industry.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Raspberries-LITRP Database: RGB Images Database for the Industrial Applications of Red Raspberries’ Automatic Quality Estimation

et al. 2022

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This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality control process to be categorized and subsequently their purchase price is determined. This selection is carried out from a sample of a complete batch to evaluate the quality of the raspberry. This database aims to solve one of the major problems in the industry: evaluating the largest amount of fruit possible and not a single sample. This major dataset enables researchers in various disciplines to develop practical machine-learning (ML) algorithms to improve red raspberry quality in the industry, by identifying different diseases and defects in the fruit, and by overcoming limitations by increasing the performance detection rate accuracy and reducing computation time. This database is made up of two packages and can be downloaded free from the Laboratory of Technological Research in Pattern Recognition repository at the Catholic University of the Maule. The RGB image package contains 286 raw original images with a resolution of 3948 × 2748 pixels from raspberry trays acquired during a typical process in the industry. Furthermore, the labeled images are available with the annotations for two diseases (86 albinism labels and 164 fungus rust labels) and two defects (115 over-ripeness labels, and 244 peduncle labels). The MATLAB code package contains three well-known ML methodological approaches, which can be used to classify and detect the quality of red raspberries. Two are statistical-based learning methods for feature extraction coupled with a conventional artificial neural network (ANN) as a classifier and detector. The first method uses four predictive learning from descriptive statistical measures, such as variance, standard deviation, mean, and median. The second method uses three predictive learning from a statistical model based on the generalized extreme value distribution parameters, such as location, scale, and shape. The third ML approach uses a convolution neural network based on a pre-trained fastest region approach (Faster R-CNN) that extracts its features directly from images to classify and detect fruit quality. The classification performance metric was assessed in terms of true and false positive rates, and accuracy. On average, for all types of raspberries studied, the following accuracies were achieved: Faster R-CNN 91.2%, descriptive statistics 81%, and generalized extreme value 84.5%. These performance metrics were compared to manual data annotations by industry quality control staff, accomplishing the parameters and standards of agribusiness. This work shows promising results, which can shed a new light on fruit quality standards methodologies in the industry.

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Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Cited by 23 publications

References 188 publications

Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer

Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Raspberries-LITRP Database: RGB Images Database for the Industrial Applications of Red Raspberries’ Automatic Quality Estimation

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