A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management

Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes

Evaluation of 1D convolutional neural network in estimation of mango dry matter content

This study empirically validates prior claims regarding the superior performance of a Convolutional Neural Network (CNN) model for estimating mango Dry Matter Content (DMC) using Near Infrared (NIR) spectroscopy. The Partial Least Squares (PLS), Artificial Neural Network (ANN), and CNN models employed in the previous publications were compared on an equal footing, i.e., employing the same training and test data, with consideration of the effect of other practices employed in those studies, i.e., outlier removal, training set partitioning, sample ordering, and spectral pretreatment and augmentation. A new benchmark RMSEP of 0.77 %FW was achieved, being statistically significant (P<0.05) different than the previously published best RMSEP for the same independent test set. This CNN model was also shown to be more robust when tested on a new season of fruit than optimised ANN and PLS models, with RMSEPs of 1.18, 2.62, and 1.87, and bias of 0.16, 2.36 and 1.56 %FW, respectively. The combination of model type and data augmentation was important, with the CNN model only slightly outperforming the ANN model when using only a second derivative pretreatment. This requirement highlights the need for chemometric input to model development. The quantification of the sensitivity of neural network model training to use of differing seeds for pseudo-random sequence generation is also recommended. The standard deviation in RMSEP of 50 ANN and CNN models trained with differing random seeds was 0.03 and 0.02 %FW, respectively

Quantification of root-knot nematode infestation in tomato using digital image analysis

Tomato is the most popular vegetable globally. However, in certain conditions, the vegetable is susceptible to plant parasites such as the root-knot nematode (RKN; Meloidogyne spp.). A proper detection method is required to identify RKN and eliminate related diseases. The traditional manual quantification of RKN using a microscope is a time-consuming and laborious task. This study aims to develop a semi-automated method to discern and quantify RKN based on size using an image analysis method. The length of RKN was assessed using three novel approaches: contour arc (CA), thin structure (TS), and skeleton graph (SG) methods. These lengths were compared with the manual measurement of RKN length. The study showed that the RKN length obtained by manual measurement was highly correlated to the length based on this method, with R2 of 0.898, 0.875, and 0.898 for the CA, TS, and SG methods, respectively. These approaches were further tested to detect RKN on 517 images. The manual and automated counting comparison revealed a coefficient of determination R2 = 0.857, 0.835 and 0.828 for CA, TS, and SG methods, respectively. The one-way ANOVA test on counting revealed F-statistic = 4.440 and p-value = 0.004. The ratio of length to width was investigated further at different ranges. The optimal result was found to occur at ratio range between 10–35. The CA, TS, and SG methods attained the highest R2 of 0.965, 0.958, and 0.973, respectively. This study found that the SG method is most suitable for detecting and counting RKN. This method can be applied to detect RKN or other nematodes on severely infected crops and root vegetables, including sweet potato and ginger. The study significantly helps in quantifying pests for rapid farm management and thus minimise crop and vegetable losses

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks

Optical Coherence Tomography (OCT) is an imperative symptomatic tool empowering the diagnosis of retinal diseases and anomalies. The manual decision towards those anomalies by specialists is the norm, but its labor-intensive nature calls for more proficient strategies. Consequently, the study recommends employing a Convolutional Neural Network (CNN) for the classification of OCT images derived from the OCT dataset into distinct categories, including Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The average k-fold (k = 10) training accuracy, test accuracy, validation accuracy, training loss, test loss, and validation loss values of the proposed model are 96.33%, 94.29%, 94.12%, 0.1073, 0.2002, and 0.1927, respectively. Fast Gradient Sign Method (FGSM) is employed to introduce non-random noise aligned with the cost function’s data gradient, with varying epsilon values scaling the noise, and the model correctly handles all noise levels below 0.1 epsilon. Explainable AI algorithms: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are utilized to provide human interpretable explanations approximating the behaviour of the model within the region of a particular retinal image. Additionally, two supplementary datasets, namely, COVID-19 and Kidney Stone, are assimilated to enhance the model’s robustness and versatility, resulting in a level of precision comparable to state-of-the-art methodologies. Incorporating a lightweight CNN model with 983,716 parameters, 2.37×108 floating point operations per second (FLOPs) and leveraging explainable AI strategies, this study contributes to efficient OCT-based diagnosis, underscores its potential in advancing medical diagnostics, and offers assistance in the Internet-of-Medical-Things

Secure Image Encryption Using Chaotic, Hybrid Chaotic and Block Cipher Approach

Secure image transmission is one of the most challenging problems in the age of communication technology. Millions of people use and transfer images for either personal or commercial purposes over the internet. One way of achieving secure image transmission over the network is encryption techniques that convert the original image into a non-understandable or scrambled form, called a cipher image, so that even if the attacker gets access to the cipher they would not be able to retrieve the original image. In this study, chaos-based image encryption and block cipher techniques are implemented and analyzed for image encryption. Arnold cat map in combination with a logistic map are used as native chaotic and hybrid chaotic approaches respectively whereas advanced encryption standard (AES) is used as a block cipher approach. The chaotic and AES methods are applied to encrypt images and are subjected to measures of different performance parameters such as peak signal to noise ratio (PSNR), number of pixels change rate (NPCR), unified average changing intensity (UACI), and histogram and computation time analysis to measure the strength of each algorithm. The results show that the hybrid chaotic map has better NPCR and UACI values which makes it more robust to differential attacks or chosen plain text attacks. The Arnold cat map is computationally efficient in comparison to the other two approaches. However, AES has a lower PSNR value (7.53 to 11.93) and has more variation between histograms of original and cipher images, thereby indicating that it is more resistant to statistical attacks than the other two approaches

Applicability of UAV in crop health monitoring using machine learning techniques

Food demands are increasing globally. Various issues such as urbanization, climate change, and desertification increasingly favour crop pests and diseases that limit crop productivity. Elaborating and discussing the pragmatic knowledge and information on recent advances in tools and techniques for crop monitoring developed in recent decades might help agronomists make more informed decisions. This chapter discusses the progress and development of new techniques equipped with recent sensors and platforms such as drones that have revolutionized the way of understanding plant physiology and stresses. It begins with the introduction to various tools available for crop stress estimation, mainly based on optical imaging such as multispectral, thermal, and hyperspectral imaging. An overview of unmanned aerial vehicle (UAV) -based image processing pipeline is presented and shed light on the possible avenues of UAV-based remote sensing for crop health monitoring using machine learning approaches

Detection and counting of root-knot nematodes using YOLO models with mosaic augmentation

Root-knot nematodes (RKN) are microscopic plant parasites that cause significant economic damage to crops and vegetables. Accurate assessment of RKN populations is required for effective management of this disease. Several “You Only Look Once” (YOLO versions 2 to 7) architectures were investigated for the application of RKN enumeration in microscopic images. YOLOv5-608 model attained Precision score of 0.960, Recall of 0.951, F1-score of 0.990, mAP of 0.972 without mosaic augmentation. Using mosaic dataset, this was increased to Precision of 1.00, Recall of 0.998, F1-score of 0.999, and mAP of 0.995. YOLOv5-608 model showed the highest correlation between the manual and machine counting of RKN: coefficient of determination (R2) of 0.991, root mean square error (RMSE) of 0.313, and coefficient of variation (CV) of 0.251. For free-living nematodes (FLN), this resulted in R2 of 0.994, RMSE of 0.058, and CV of 1.760. YOLOv7-608 achieved the highest correlation between manual and machine counting of overlapped RKN (R2 of 0.970, RMSE of 0.595, and CV of 0.123). In addition, this study explored a new application of mosaic augmentation to analyse microscopic images acquired with different objective lense magnifications. The proposed framework supports the rapid assessment of plant parasitic nematodes necessary to implement nematode control strategies and improve crop management practices

Recent advances in crop disease detection using UAV and deep learning techniques

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection

Stock price forecasting with deep learning: A comparative study

The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the cooperative deep-learning architecture proposed by us. Our experiments show that: (1) both LSTM and GRU are circumstantial in stock forecasting if only the stock market features are used; (2) the performance of LSTM and GRU for stock price forecasting can be significantly improved by incorporating the financial news sentiments with the stock features as the input; (3) both the LSTM-News and GRU-News models are able to produce better forecasting in stock price equally; (4) the cooperative deep-learning architecture proposed in this study could be modified as an expert system incorporating both the LSTM-News and GRU-News models to recommend the best possible forecasting whichever model can produce dynamically

BotanicX-AI: Identification of tomato leaf diseases using an explanation-driven deep-learning model

Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice