Qiu Zhichao, Li Gangao, Huang Zongbao, He Xiuhan, Zhang Zilin, Li Zhiwei, Du Huiling
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China.
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, China.
Front Plant Sci. 2024 May 30;15:1354290. doi: 10.3389/fpls.2024.1354290. eCollection 2024.
Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.
农产品水分含量检测对于质量控制、加工效率和储存管理至关重要。检测谷子水分含量可确保谷子质量稳定，并帮助农民确定最佳收获时间。基于STM32和PCAP01电容数字转换器芯片设计了一种差分电容式水分含量检测装置。选择电容法结合反向传播（BP）算法和极限学习机（ELM）算法来构建谷子水分含量、温度和体积占空比的分析模型。这项工作对具有不同水分含量、温度以及被测物质占据检测区域比例（即体积占空比）的谷子进行电容测量。在此基础上，使用麻雀搜索算法（SSA）对BP和ELM模型进行优化。然而，由于后期迭代中种群多样性降低，SSA可能会遇到陷入局部最优解等问题。因此，引入逻辑算法对SSA进行优化，使其更适合解决特定问题。经过对比分析，使用逻辑-SSA-ELM算法预测的模型更准确。结果表明，预测集决定系数（RP）、预测集均方根误差（RMSEP）和预测集比率性能偏差（RPDP）的预测值分别为0.7016、3.7150和1.4035。该算法具有优异的预测性能，可作为谷子水分含量检测模型。鉴于谷子水分含量检测在收购和储存中的重要作用，研究一种无损、快速的在线实时检测方法尤为重要。所设计的具有差分结构的电容式传感器具有良好的稳定性和高精度，可进一步深入研究，并逐步朝着智能农业和精准农业的农业发展总体趋势迈进。
