Deep Learning for Postharvest Decay Prediction in Apples

Stasenko, Nikita; Savinov, Maxim; Burlutskiy, Valeriy; Pukalchik, Mariia; Somov, Andrey

doi:10.1109/iecon48115.2021.9589498

Cited by 9 publications

(5 citation statements)

References 47 publications

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“…The integration of advanced technologies in agriculture has witnessed a transformative shift, particularly with the convergence of dynamic mode decomposition, deep learning, and various sensing technologies. Stasenko et al [1] and Gao et al [2] have made significant contributions in this regard, focusing on decay prediction in apples and heightvariable monocular vision ranging technology, respectively. These studies underscore the growing trend of leveraging sophisticated algorithms and vision technologies in agriculture to enhance efficiency and precision.…”

Section: In-depth Review Of Models Used For Multimodal Analysis Of Cr...mentioning

confidence: 99%

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

Munaganuri,

Rao

2024

IEEE Access

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The growing necessity for sustainable agriculture in the face of escalating global food demands and climate change underscores the importance of optimizing crop water usage. Current methodologies for estimating crop water requirements, primarily relying on traditional remote sensing and empirical models, face limitations in precision, adaptability to diverse crop types, and dynamic environmental conditions. This paper introduces an innovative machine learning model designed to accurately estimate crop water requirements and optimize irrigation scheduling using remote sensing data samples. The model's robustness stems from its sophisticated pre-processing technique, employing a Grey Wolf Optimized (GWO)-based Non-Local Means (NLM) algorithm for denoising images sourced from various remote sensing technologies. Subsequently, these refined images undergo a transformation into multidomain features through the integration of a Vision Transformer, Fourier, Entropy, Color Map, and Gabor Maps. This multi-faceted approach ensures a comprehensive analysis of the data, capturing intricate details pertinent to crop water requirements.A key innovation in our model is the implementation of the Dual Elephant Herding Optimization (DEHO) Process. This optimizer adeptly selects features by minimizing intra-class differences and maximizing interclass variance, thereby enhancing the model's discriminative capabilities. The selected features are then classified into distinct irrigation schedule classes using a Deep Dyna Q Graph Convolutional Network (DDQGCN). This network not only augments the efficiency of classifying images from diverse sources but also bolsters the model's capability to adapt to varying crop types and environmental conditions. The efficacy of the proposed model is further augmented by incorporating vector autoregressive moving average with eXogenous inputs (VARMAx) algorithms. This addition enables the conversion of output into temporal features, facilitating precise predictions of soil quality and the dynamic adjustment of irrigation schedules. Empirical testing of this model across different geographies on mango, rice, and cotton crops demonstrated significant improvements over existing methods. The model exhibited an 8.5% increase in precision, 8.3% in accuracy, 4.9% in recall, 5.5% in Area Under the Curve (AUC), and 4.5% in specificity, alongside a 3.5% reduction in response delays. Particularly noteworthy is the model's enhanced pre-emption capabilities, reflected in a 2.9% higher precision of pre-emption, 2.5% greater accuracy, 1.5% increased recall, 2.4% higher AUC of pre-emption, 1.5% improved specificity, and a 1.9% reduction in delay compared to existing methodologies.

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Section: In-depth Review Of Models Used For Multimodal Analysis Of Cr...mentioning

confidence: 99%

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

Munaganuri,

Rao

2024

IEEE Access

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“…The datasets for plant phenotyping are publicly available datasets, mainly used for plant disease recognition (Singh et al., 2018), crop yield estimation (David et al., 2021), horticulture fruit object detection (Stasenko et al., 2021), and so on. With the in‐depth study of plant phenotypes and species‐specific phenotypic analysis, there are new requirements for phenotype image datasets.…”

Section: Basic Concepts Of Meta‐learning and Few Available Samplesmentioning

confidence: 99%

Meta‐learning shows great potential in plant disease recognition under few available samples

Deng

Wang

et al. 2023

The Plant Journal

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Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, metalearning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.

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“…In this work, we implement a Mask R-CNN model due to the Feature Pyramid Network (FPN) and ResNet101 backbone, which allow for generating the bounding boxes (object detection) and segmentation masks (instance segmentation). In [45], we have compared the Mask R-CNN to such applied CNN-based models as U-Net [46] and Deeplab [47] for early postharvest decay detection, and Mask R-CNN achieved the highest performance in terms of average precision, namely 67.1% against 59.7% and 56.5%, respectively. Moreover, the Mask R-CNN model generates the bounding boxes and segmentation masks of the postharvest decay and fungal zones separately from each other.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples

Stasenko

Shukhratov

Savinov

et al. 2023

Entropy

Self Cite

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Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples. The Pix2PixHD model achieved higher results in terms of VNIR images translation from RGB (SSIM = 0.972). Mask R-CNN model was selected as a CNN technique for VNIR images segmentation and achieved 58.861 for postharvest decay zones, 40.968 for fungal zones and 94.800 for both the decayed and fungal zones detection and prediction in stored apples in terms of F1-score metric. In order to verify the effectiveness of this approach, a unique paired dataset containing 1305 RGB and VNIR images of apples of four varieties was obtained. It is further utilized for a GAN model selection. Additionally, we acquired 1029 VNIR images of apples for training and testing a CNN model. We conducted validation on an embedded system equipped with a graphical processing unit. Using Pix2PixHD, 100 VNIR images from RGB images were generated at a rate of 17 frames per second (FPS). Subsequently, these images were segmented using Mask R-CNN at a rate of 0.42 FPS. The achieved results are promising for enhancing the food study and control during the postharvest stage.

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Deep Learning for Postharvest Decay Prediction in Apples

Cited by 9 publications

References 47 publications

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

PAMICRM: Improving Precision Agriculture Through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples

Meta‐learning shows great potential in plant disease recognition under few available samples

Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples

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