Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage.

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control.

Anthracnose disease, caused by fungi of the genus Colletotrichum, poses a major threat to mango production and export industries, with Colletotrichum asian

This research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development. In the algorithmic model development, root mean square propagation was used as the solver to train the neural network, given 150 epochs. In addition, spectral raw data was preprocessed to transform it into an understandable and efficient format. The optimal classification model was the 2B-CNN model with 1st-derivative preprocessing, achieving an accuracy of 0.94 for the calibration set and 0.71 for the prediction set. The proposed NIR-HSI/2B-CNN scheme could detect anthracnose mangoes since the the first day of inoculation of the spore suspension (i.e., day 0) through to day 3, achieving a moderate classification accuracy. Meanwhile, the accuracy of conventional convolutional neural networks (CNN) were within a range of 0.66-0.67 for the calibration set and 0.55-0.57 for the prediction set. The results indicated that incorporating spatial features in the 2B-CNN modeling enhanced the prediction performance of the algorithm. The proposed NIR-HSI/2B-CNN algorithmic scheme needs refinements to be able to reliably sort mango fruits into those suitable for premium fresh consumption and export without anthracnose and those for domestic consumption or processing. The novelty of this research lies in the use of NIR-HSI and 2B-CNN algorithm to detect plant pathogens at the early stages of disease development. In addition, the new method of natural simulation to deposit the fungal spores onto the mango surface by spraying spore suspension onto the mango surface where the conidia penetrated unaided into the underneath of the mango peel is proposed.

Artificial Intelligence (AI), Agricultural Machine Learning (AML) and Deep Learning in Agriculture (ADL) are increasingly applied for crop health monitoring and precision disease management. Huanglongbing (HLB) or citrus greening disease, is among the most destructive citrus diseases worldwide, causing substantial yield and quality losses. This study developed a non-destructive UAV-based multispectral sensing framework for real-time HLB detection and severity classification using spectral signatures and machine learning algorithms. Multispectral imagery was acquired using a Parrot Sequoia sensor mounted on a hexacopter UAV during the peak vegetative stage at Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, India. Spectral information from Green (550 nm), Red (660 nm), Red Edge (735 nm), and Near Infrared (790 nm) bands was used to characterize Healthy, 25% HLB, 50% HLB, 100% HLB, and Background classes. A total of 10,000 multispectral canopy patches (2,000 samples per class; 128 × 128 pixels) were extracted for analysis. Vegetation indices including NDVI, NDRE, and GNDVI were computed to evaluate canopy stress conditions. Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) classifiers were trained and evaluated using multispectral reflectance features. RF achieved the highest overall accuracy (92.45%), followed by KNN (92.05%) and SVM (91.10%), with macro-average precision, recall, and F1-score values of 92%. Multiclass ROC analysis produced micro- and macro-average AUC values of 0.99. Spectral analysis showed higher Red Edge and NIR reflectance in healthy canopies, whereas HLB-infected canopies exhibited increased Red reflectance and reduced NIR response due to chlorophyll degradation and cellular damage. ANOVA confirmed significant differences among classifiers (p < 0.05). UAV-based multispectral sensing enabled rapid and non-destructive detection of Huanglongbing severity in citrus orchards.

Artificial Intelligence (AI), Agricultural Machine Learning (AML) and Deep Learning in Agriculture (ADL) are increasingly applied for crop health monitoring and precision disease management. Huanglongbing (HLB) or citrus greening disease, is among the most destructive citrus diseases worldwide, causing substantial yield and quality losses. This study developed a non-destructive UAV-based multispectral sensing framework for real-time HLB detection and severity classification using spectral signatures and machine learning algorithms. Multispectral imagery was acquired using a Parrot Sequoia sensor mounted on a hexacopter UAV during the peak vegetative stage at Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, India. Spectral information from Green (550 nm), Red (660 nm), Red Edge (735 nm), and Near Infrared (790 nm) bands was used to characterize Healthy, 25% HLB, 50% HLB, 100% HLB, and Background classes. A total of 10,000 multispectral canopy patches (2,000 samples per class; 128 × 128 pixels) were extracted for analysis. Vegetation indices including NDVI, NDRE, and GNDVI were computed to evaluate canopy stress conditions. Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) classifiers were trained and evaluated using multispectral reflectance features. RF achieved the highest overall accuracy (92.45%), followed by KNN (92.05%) and SVM (91.10%), with macro-average precision, recall, and F1-score values of 92%. Multiclass ROC analysis produced micro- and macro-average AUC values of 0.99. Spectral analysis showed higher Red Edge and NIR reflectance in healthy canopies, whereas HLB-infected canopies exhibited increased Red reflectance and reduced NIR response due to chlorophyll degradation and cellular damage. ANOVA confirmed significant differences among classifiers (p < 0.05). UAV-based multispectral sensing enabled rapid and non-destructive detection of Huanglongbing severity in citrus orchards.