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This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new techniques for these tasks is a vital research area. Here, deep learning has established the current state of the art. However, deploying large-capacity deep models on-board an Earth observation satellite poses additional technological challenges concerned with their memory footprints, energy consumption requirements, and robustness against varying-quality image data, with the last problem being under-researched. In this paper, we tackle this issue, and propose a set of simulation scenarios that reflect a range of atmospheric conditions and noise contamination that may ultimately happen on-board an imaging satellite. We verify their impact on the generalization capabilities of spectral and spectral-spatial convolutional neural networks for hyperspectral image segmentation. Our experimental analysis, coupled with various visualizations, sheds more light on the robustness of the deep models and indicate that specific noise distributions can significantly deteriorate their performance. Additionally, we show that simulating atmospheric conditions is key to obtaining the learners that generalize well over image data acquired in different imaging settings.

Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.

Food quality and safety are a great public concern; outbreaks of food-borne illnesses can lead to different health problems. Consequently, rapid and non-destructive artificial intelligence approaches are required for sensing the safety situation of foods. As a promising technology, deep learning for hyperspectral imaging (HSI) has the potential for rapid food safety and quality evaluation and control. Spectral signatures of food substances are sensitive to water content variation, the extent of hydrogen bonding, geographical origin, harvesting time and the variety of food under study. Deep learning models have shown great potential in addressing the challenge of sensitivity of spectral signatures of food substances. After discussing the basics of HSI, this review provides a detailed study of various deep-learning algorithms that have been put to use via HSI in the determination of sensory and physicochemical properties, adulteration and microbiological contamination of food products. The existing literature includes HSI for evaluating quality attributes and safety of different food categories like fruits, vegetables, cereals, milk and meat. This paper presents a practical framework for deep learning-based food quality assessment using hyperspectral imagery. We demonstrate its versatility across diverse food quality domains and provide a concise step-by-step guide for researchers. It has been predicted that deep learning for HSI can be considered a reliable alternative technique to conventional methods in realising rapid and accurate inspection, for testing food quality and safety.

This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

In order to help farmers and crop managers better understand the elements influencing crop status and growth, hyperspectral and multispectral data processing methods have shown to be beneficial. Utilising advanced computational methods via machine learning is one strategy that has been in use recently. This method can forecast satellite image data based on the circumstances of mapping different types of land and vegetation in the field. This research proposes novel technique in sustainable agriculture-based climate change detection using hyperspectral image analysis with machine learning model. Here, the hyperspectral image of agricultural field is collected as input and processed for smoothening with normalisation. The proposed image analysis model is carried out in two stages which is feature extraction and classification. In stage 1, the feature extraction of processed input hyperspectral image is carried out using multilayer Bayesian encoder vector model (MBEV). The second stage of this proposed model is to classify the extracted image using deep convolutional belief neural networks (DCBNN). The experimental analysis has been carried out for various agriculture-based hyperspectral image datasets in terms of training accuracy, sensitivity, specificity, and AUC. The experimental findings demonstrate that, when compared to other ways, the suggested strategy performed exceptionally well. Proposed technique attained training accuracy of 97%, AUC of 85%, sensitivity of 96%, and specificity of 93%.

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits.

A key component of managing natural resources is the use and cover of the land. Maps of environmental changes are created using it in order to monitor ecosystems. For forestry, urban planning and agriculture, automatic mapping has many benefits. The science of remote sensing has benefited greatly from the development of deep learning techniques, which have yielded impressive results in image classification. This research proposes novel technique in ecosystem monitoring with disaster management in urban environments based on hyperspectral image analysis using a machine learning model. Here the input has been collected as hyperspectral image as well as processed for noise removal, normalisation and smoothening. Ecosystem monitoring is carried out utilizing Gaussian attention linear discriminant logistic regression. Then, the disaster management has been carried out based on the ecosystem monitoring model using cloud-based genetic spatio algorithm. Experimental analysis is carried out in terms of prediction accuracy, precision, F-measure, recall and RMSE for various hyperspectral images: prediction accuracy of 96%, precision of 97%, F-measure of 88%, recall of 95% and RMSE of 60% for proposed technique.

Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances and thus do not take into account the possible local spatial correlation. When the pixels that lie on the boundaries of different materials or the inhomogeneous region, the abundances of the neighboring pixels do not have those prior constraints. Thus, we propose a novel GMM unmixing method based on superpixel segmentation (SS) and low-rank representation (LRR), which is called GMM-SS-LRR. we adopt the SS in the first principal component of HSI to get the homogeneous regions. Moreover, the HSI to be unmixed is partitioned into regions where the statistical property of the abundance coefficients have the underlying low-rank property. Then, to further exploit the spatial data structure, under the Bayesian framework, we use GMM to formulate the unmixing problem, and put the low-rank property into the objective function as a prior knowledge, using generalized expectation maximization to solve the objection function. Experiments on synthetic datasets and real HSIs demonstrated that the proposed GMM-SS-LRR is efficient compared with other current popular methods.