AgriEngineering

This study developed identification models for five domestic rice varieties—Akitakomachi (Akita 31), Hitomebore (Tohoku 143), Hinohikari (Nankai 102), Koshihikari (Etsunan 17) and Nanatsuboshi (Soriku 163)—using fluorescence spectroscopy, near-infrared (NIR) spectroscopy, and machine learning. Two-dimensional fluorescence images were generated from excitation emission matrix (EEM) spectra in the 250–550 nm and 900–1700 nm ranges. Four machine learning hybrid models combining a convolutional neural network (CNN) with k-nearest neighbor algorithm (KNN), random forest (RF), logistic regression (LR), and support vector machine (SVM), were constructed using Python (ver. 3.13.2) by integrating feature extraction from CNN with traditional algorithms. The performances of KNN, RF, LR, and SVM were compared with NIR spectra. The NIR+KNN model achieved 0.9367 accuracy, while the fluorescence fingerprint+CNN model reached 0.9717. The CNN+KNN model obtained the highest mean accuracy (0.9817). All hybrid models outperformed individual algorithms in discrimination accuracy. Fluorescence images revealed at 280 nm excitation/340 nm emission linked to tryptophan, and weaker peaks at 340 nm excitation/440 nm emission, likely due to advanced glycation end products. Hence, combining fluorescent fingerprinting with deep learning enables accurate, reproducible rice variety identification and could prove useful for assessing food authenticity in other agricultural products.

Most fruit classification studies rely on color-based features, but shape-based analysis provides a promising alternative for distinguishing subtle variations within the same variety. Tomato shape classification is challenging due to irregular contours, variable imaging conditions, and difficulty in extracting consistent geometric features. In this study, we propose an efficient and structured workflow to address these challenges through contour-based analysis. The process begins with the application of a Mask Region-based Convolutional Neural Network (Mask R-CNN) model to accurately isolate tomatoes from the background. Subsequently, the segmented tomatoes are extracted and encoded using Elliptic Fourier Descriptors (EFDs) to capture detailed shape characteristics. These features are used to train a range of machine learning models, including Support Vector Machine (SVM), Random Forest, One-Dimensional Convolutional Neural Network (1D-CNN), and Bidirectional Encoder Representations from Transformers (BERT). Experimental results observe that the Random Forest model achieved the highest accuracy of 79.4%. This approach offers a robust, interpretable, and quantitative framework for tomato shape classification, reducing manual labor and supporting practical agricultural applications.

Chemical defoliation is an important management practice in cotton to facilitate mechanical harvesting and leaf removal and maintain lint quality. Recent advances in precision agriculture have enabled the development of autonomous robotic platforms with a targeted side-spraying system that can achieve good canopy penetration while preventing soil compaction and crop mechanical damage. A side-wise spraying system allows for application of defoliant at different canopy heights. However, information on the effects of staggered defoliation on cotton fiber quality is limited. Thus, field research was conducted to evaluate the effects of various staggered application timing intervals (15, 10, 8, 5, and 3 days) on fiber quality and compare them with standard over-the-top broadcast applications. Staggered defoliation affected fiber length, with significant differences observed for upper half mean length, fiber length based on weight, and upper quartile length. Fiber maturity was also influenced by staggered defoliation timing, with a 15-day interval resulting in the lowest micronaire and higher immature fiber content. The effects of staggered defoliation on other parameters, such as strength, uniformity, and trash characteristics, varied across locations. The findings highlight the potential of robotic systems for chemical spraying and emphasize the need for further research on more precise and targeted application of defoliants to improve fiber quality.