Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning

Kundu, Nidhi; Rani, Geeta; Dhaka, Vijaypal Singh; Gupta, Kalpit; Nayak, S. Chandra; Vocaturo, Eugenio; Zumpano, Ester

doi:10.1016/j.aiia.2022.11.002

Cited by 29 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperspectral imaging system intermixed with the chemometric methods, is recognized as a high efficiency, speedy, economical and practical, and nondestructive detection technology [ 40 , 63 ]. In this study, BPNN, SVM and RF were chosen to develop the recognition models for strawberry diseases using different features.…”

Section: Methodsmentioning

confidence: 99%

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging

Zhang,

Ou,

et al. 2023

Plant Methods

View full text Add to dashboard Cite

Background Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. Results Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. Conclusions This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.

show abstract

Section: Methodsmentioning

confidence: 99%

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging

Zhang,

Ou,

et al. 2023

Plant Methods

View full text Add to dashboard Cite

show abstract

“…Early disease identification is essential for implementing effective control measures, thereby minimizing crop losses and reducing the need for chemical interventions 18 . Over the years, researchers have explored a range of methodologies and technologies to enhance the precision, speed, and scalability of plant disease diagnosis 19 , 20 .…”

Section: Introductionmentioning

confidence: 99%

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Wang,

Liu

2024

Sci Rep

View full text Add to dashboard Cite

This study introduces YOLOv8n-vegetable, a model designed to address challenges related to imprecise detection of vegetable diseases in greenhouse plant environment using existing network models. The model incorporates several improvements and optimizations to enhance its effectiveness. Firstly, a novel C2fGhost module replaces partial C2f. with GhostConv based on Ghost lightweight convolution, reducing the model’s parameters and improving detection performance. Second, the Occlusion Perception Attention Module (OAM) is integrated into the Neck section to better preserve feature information after fusion, enhancing vegetable disease detection in greenhouse settings. To address challenges associated with detecting small-sized objects and the depletion of semantic knowledge due to varying scales, an additional layer for detecting small-sized objects is included. This layer improves the amalgamation of extensive and basic semantic knowledge, thereby enhancing overall detection accuracy. Finally, the HIoU boundary loss function is introduced, leading to improved convergence speed and regression accuracy. These improvement strategies were validated through experiments using a self-built vegetable disease detection dataset in a greenhouse environment. Multiple experimental comparisons have demonstrated the model's effectiveness, achieving the objectives of improving detection speed while maintaining accuracy and real-time detection capability. According to experimental findings, the enhanced model exhibited a 6.46% rise in mean average precision (mAP) over the original model on the self-built vegetable disease detection dataset under greenhouse conditions. Additionally, the parameter quantity and model size decreased by 0.16G and 0.21 MB, respectively. The proposed model demonstrates significant advancements over the original algorithm and exhibits strong competitiveness when compared with other advanced object detection models. The lightweight and fast detection of vegetable diseases offered by the proposed model presents promising applications in vegetable disease detection tasks.

show abstract

“…The results showed that YOLOv4 could achieve swift and precise disease detection capabilities. Kundu et al (2022) [ 24 ] presented a study on disease detection and severity prediction in maize crops. Paymode and Malode (2022) [ 25 ] conducted research on multi-crop leaf disease image classification using transfer learning.…”

Section: Introductionmentioning

confidence: 99%

An efficient deep learning model for tomato disease detection

Wang,

Liu

2024

Plant Methods

View full text Add to dashboard Cite

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR’s self-attention mechanism to craft a backbone feature extraction network, enhancing the model’s capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network’s ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.

show abstract

Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning

Cited by 29 publications

References 22 publications

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging

Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

An efficient deep learning model for tomato disease detection

Contact Info

Product

Resources

About