José Miguel Molina-Martínez

Accurate automatic classification of seed varieties is important for seed sorting, quality assurance, and plant breeding, yet reliable discrimination remains difficult when cultivars exhibit highly similar visual characteristics. This study presents a reproducible and interpretable framework for the binary classification of two Turkish pumpkin seed varieties using tabular morphological descriptors extracted from segmented seed images. Unlike many previous machine learning studies in this domain, which offer limited interpretability and leave model decisions largely as a black box, the proposed approach places Explainable Artificial Intelligence (XAI) at the center of the analysis. The framework combines biologically meaningful feature engineering, Optuna-based hyperparameter optimization, repeated stratified cross-validation, and a comparative evaluation of XGBoost, LightGBM, and CatBoost. Model explainability was investigated using SHapley Additive exPlanations (SHAP) to identify the morphological traits driving both global and instance-level predictions, while corrected repeated k-fold t-tests were used to assess the statistical significance of performance differences, which confirmed comparable accuracy among the three boosting models and a significant advantage over the baseline classifiers. All three boosting ensembles consistently outperformed the baseline classifiers (SVM, Logistic Regression, and Random Forest) on the hold-out test set. CatBoost achieved the best overall results, with an accuracy of 0.888, an F1-score of 0.879, and an MCC of 0.777. SHAP analysis consistently highlighted compactness, roundness, eccentricity, and engineered interaction descriptors as the most influential predictors. Overall, the proposed XAI-driven framework provides an accurate and transparent solution for pumpkin seed classification.

Recent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550–900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7‐layer network (CNN‐7) and a deep 18‐layer network (CNN‐18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D‐CNN‐18, compared to 90% for 3D‐CNN‐7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting. Practical Application: Orange bruises can reduce the market value of food, which is why the food processing industry needs to carry out quality inspections. An effective way to perform this inspection is by using hyperspectral images that can be processed with 2D or 3D models, either with deep or shallow neural networks. The results of the comparison performed in this work can be useful for the development of more accurate and efficient bruise detection methods for fruit inspection.

Due to rising food demand and the limitation of water resources, achieving water security is essential. The lettuce crop is affected when grown under limited water supplies as it produces small heads, especially during the late growing stage. For this reason, it is important to maximize water use efficiency and crop productivity. Two successive experiments were conducted during 2021 and 2022 to reduce losses via deep percolation using a geotextile layer at different soil depths under different irrigation levels of the lettuce crop (Lactuca sativa L. var. capitata). This study aims to reduce water losses due to deep percolation and improve crop growth and yield parameters for iceberg lettuce under subsurface drip irrigation in sandy loam soil conditions. In order to achieve these aims, different amounts of irrigation (100, 80, and 60% of crop evapotranspiration “ETc”) and a geotextile layer at different soil depths (20, 30, and 40 cm from the soil surface) were used. The results revealed that the use of a geotextile layer with 20 and 30 cm depths significantly improved irrigation application efficiency and noticeably increased soil water content in the root zone. The observed results during both seasons showed that geotextile layers at 20, 30, and 40 cm depths under irrigation of 100% ETc significantly increased vegetative growth characteristics (plant height, head diameter, head circumference, head volume, plant fresh weight, and leaf area) and crop productivity compared to the control (without geotextile). In particular, the geotextile layer at a 30 cm depth under irrigation of 100% of ETc was the most statistically effective treatment in this study, with yield values of 69.3 and 67.5 t ha⁻¹ in the two seasons, respectively. However, the treatments of geotextile layers at 20 and 30 cm depths under irrigation of 80% of ETc also recorded statistically effective results for crop growth parameters and yield in this study. In general, geotextiles can be used at different depths as an irrigation management practice to reduce deep percolation in the field.

Nitrogen is an essential macronutrient for the growth and development of tomatoes. However, excess nitrogen fertilization can affect the quality of tomato fruit, making it unattractive to consumers. Consequently, the aim of this study is to develop a method for the early detection of excessive nitrogen fertilizer use in Royal tomato by visible and near-infrared spectroscopy. Spectral reflectance values of tomato leaves were captured at wavelengths between 400 and 1100 nm, collected from several treatments after application of normal nitrogen and on the first, second, and third days after application of excess nitrogen. A new method based on convolutional neural networks (CNN) with an attention mechanism was proposed to perform the estimation of nitrogen overdose in tomato leaves. To verify the effectiveness of this method, the proposed attention mechanism-based CNN classifier was compared with an alternative CNN having the same architecture without integrating the attention mechanism, and with other CNN models, AlexNet and VGGNet. Experimental results showed that the CNN with an attention mechanism outperformed the alternative CNN, achieving a correct classification rate (CCR) of 97.33% for the treatment, compared with a CCR of 94.94% for the CNN alone. These findings will help in the development of a new tool for rapid and accurate detection of nitrogen fertilizer overuse in large areas.

Understanding the anticipated impact of climate change on agriculture, as well as water conservation, is critical to achieving food security. Therefore, during this critical time and due to reduced water resources and increased food demand, it is important to study the impact of water-stress conditions on crops. Two successive seasons were carried out through the 2021 and 2022 seasons to estimate the crop coefficient (K_c) and study the effect of abscisic acid on red cabbage plants (Brassica oleracea var. capitata) under water-stress conditions at a private farm in the Bilbeis region, Sharqia Governorate, Egypt. The aim was to estimate the crop coefficient (K_c) and effect of different concentrations of abscisic acid (ABA) (0, 25, 50, and 75 ppm) under various irrigation levels (100, 80, and 60% of field capacity “FC”) on the growth process and yield parameters of red cabbage plants. The results revealed that the average estimated crop coefficient (K_c) for red cabbage crops under standard conditions, 100% of FC, was 0.75, 1.07, 1.2 and 0.88 and 0.77, 1.2, 1.25 and 0.82 for Initial, Development, Mid, and End stages during the 2021 and 2022 growing seasons, respectively. Data collected during both seasons clearly showed that all treatments significantly increased both the plant’s growth process and yield parameters when compared to the control. However, abscisic acid (ABA, 75 ppm) application with irrigation requirements (80% of FC) was statistically the most effective treatment in this study. Hence, this means a water savings of 20% can be achieved without significantly compromising the yield.

Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400–1100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions.

The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples.

The challenge today is to optimize agriculture water consumption and minimize leaching of pollutants in agro-ecosystems in order to ensure a sustainable agriculture. The use of different technologies and the adoption of different irrigation strategies can facilitate efficient fertigation management. In this respect, the determination of soil field capacity point is of utmost importance. The use of a portable weighing lysimeter allows an accurate quantification of crop water consumption and water leaching, as well as the detection of soil field capacity point. In this work, a novel algorithm is developed to obtain the soil field capacity point, in order to give autonomy and objectivity to efficient irrigation management using a portable weighing lysimeter. The development was tested in field grown horticultural crops and proved to be useful for optimizing irrigation management.

Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.

To achieve healthy and optimal yields of agricultural products, the principles of nutrition must be observed and appropriate fertilizers must be applied. Nutritional deficiencies or overabundance reduce the quality and yield of the products. Thus, their early detection prevents physiological disorders and associated diseases. Most research efforts have focused on spectroscopy, which extracts only spectral data from a single point of the product. The present study aims to detect early excess nitrogen in cucumber plants by using a new hyperspectral imaging technique based on a hybrid of artificial neural networks and the imperialist competitive algorithm (ANN-ICA), which can provide spectral and spatial information on the leaves at the same time. First, cucumber seeds were planted in 18 pots. The same inputs were applied to all the pots until the plants grew; after that, 30% excess nitrogen was applied to nine pots with irrigation water, while it remained constant in the other nine pots. Each day, six leaves were collected from each pot, and their images were captured using a hyperspectral camera (in the range of 400–1100 nm). The wavelengths of 715, 783 and 821 nm were determined as the most effective for early detection of excess nitrogen using a hybrid of artificial neural networks and the artificial bee colony algorithm (ANN-ABC). The parameter of days of treatment was classified using ANN-ICA. The performance of the classifier was evaluated using different criteria, namely recall, accuracy, specificity, precision and the F-measure. The results indicate that the differences between different days were statistically significant. This means that the hyperspectral imaging technique was able to detect plants with excess nitrogen in the near-infrared range (NIR), with a correct classification rate of 96.11%.