The maturity classification of specialty crops such as strawberries and
tomatoes is an essential agricultural downstream activity for selective
harvesting and quality control (QC) at production and packaging sites. Recent
advancements in Deep Learning (DL) have produced encouraging results in color
images for maturity classification applications. However, hyperspectral imaging
(HSI) outperforms methods based on color vision. Multivariate analysis methods
and Convolutional Neural Networks (CNN) deliver promising results; however, a
large amount of input data and the associated preprocessing requirements cause
hindrances in practical application. Conventionally, the reflectance intensity
in a given electromagnetic spectrum is employed in estimating fruit maturity.
We present a feature extraction method to empirically demonstrate that the peak
reflectance in subbands such as 500-670 nm (pigment band) and the wavelength of
the peak position, and contrarily, the trough reflectance and its corresponding
wavelength within 671-790 nm (chlorophyll band) are convenient to compute yet
distinctive features for the maturity classification. The proposed feature
selection method is beneficial because preprocessing, such as dimensionality
reduction, is avoided before every prediction. The feature set is designed to
capture these traits. The best SOTA methods, among 3D-CNN, 1D-CNN, and SVM,
achieve at most 90.0 % accuracy for strawberries and 92.0 % for tomatoes on our
dataset. Results show that the proposed method outperforms the SOTA as it
yields an accuracy above 98.0 % in strawberry and 96.0 % in tomato
classification. A comparative analysis of the time efficiency of these methods
is also conducted, which shows the proposed method performs prediction at 13
Frames Per Second (FPS) compared to the maximum 1.16 FPS attained by the
full-spectrum SVM classifier.

草莓和番茄等特殊作物的成熟度分类对于选择性采摘和质量控制是农业下游的重要活动。本文提出了一种特征提取方法，展示了在特征选择方面的最新成果，并通过比较分析表明该方法在成熟度分类和时间效率方面的表现优于现有方法。

利用高光谱成像进行特种作物成熟度分类的双频带特征选择

Dual-band feature selection for maturity classification of specialty  crops by hyperspectral imaging

The responsible and sustainable agave-tequila production chain is fundamental
for the social, environment and economic development of Mexico's agave regions.
It is therefore relevant to develop new tools for large scale automatic agave
region monitoring. In this work, we present an Agave tequilana Weber azul crop
segmentation and maturity classification using very high resolution satellite
imagery, which could be useful for this task. To achieve this, we solve
real-world deep learning problems in the very specific context of agave crop
segmentation such as lack of data, low quality labels, highly imbalanced data,
and low model performance. The proposed strategies go beyond data augmentation
and data transfer combining active learning and the creation of synthetic
images with human supervision. As a result, the segmentation performance
evaluated with Intersection over Union (IoU) value increased from 0.72 to 0.90
in the test set. We also propose a method for classifying agave crop maturity
with 95\% accuracy. With the resulting accurate models, agave production
forecasting can be made available for large regions. In addition, some
supply-demand problems such excessive supplies of agave or, deforestation,
could be detected early.

本文提出一种基于高分辨率卫星图像的 Agave tequilana Weber azul 作物分割和成熟度分类的方法，结合主动学习和人工监督创建合成图像，解决数据不足、标签质量差、数据不平衡和模型表现差等问题，将产量预测和供需问题检测结合，大规模监测鸢节生产链的实现。