使用机器学习算法预测西红品种的水果大小
Masaaki Takahashi1, Yasushi Kawasaki1, Hiroki Naito1,2
1Research Center for Agricultural Robotics, National Agricultural and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan.
Frontiers in plant science
|February 13, 2025
概括
使用机器学习对温室番茄果实大小的早期预测有助于种植者管理产量. 斜坡回归模型可以准确预测收获规模,减少小水果的比例,改善园艺供应链.
科学领域:
- 园艺科学 园艺科学
- 农业工程 农业工程
- 数据科学数据科学数据科学
背景情况:
- 准确的早期预测番茄果实大小对于温室种植管理和供应链效率至关重要.
- 减少小型番茄的产量是种植者的主要目标.
- 机器学习为开发农业预测模型提供了潜力.
研究的目的:
- 开发和评估机器学习模型,以便在收获时预测番茄果实大小.
- 为了比较回归,额外树回归和CatBoost回归模型的性能.
- 评估整合平均温度对预测准确性的影响.
主要方法:
- 利用水果直径数据随时间推移,以及分析后的累积温度来估计水果的重量.
- 使用PyCaret训练和评估了三种机器学习模型 (回归,额外树回归,CatBoost回归).
- 在三个番茄品种 ("CF Momotaro York"",Zayda"",Adventure") 上测试模型,使用不同的预测期.
主要成果:
- 在使用200°C d,300°C d和500°C d的数据时,Ridge Regression为"Zayda"获得了最低的平均绝对百分比误差 (MAPE) 9.8%,为200°C d,300°C d和500°C d.
- 延长预测期 (300°C d,500°C d,800°C d) 提高了所有回归品种的准确性 (例如",Zayda"的8.8%).
- 在预测期内添加平均温度稍微提高了模型性能.
结论:
- 机器学习模型,特别是回归,可以有效地预测温室番茄在收获时的尺寸.
- 早期果实大小预测有助于种植管理,例如果实稀释.
- 自动化水果直径数据采集可以进一步提高这些预测模型的实用性.
更多相关视频
06:26An Efficient Clearing Protocol for the Study of Seed Development in Tomato Solanum lycopersicum L.
Published on: September 7, 2022
4.0K
10:25Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
10.6K
相关概念视频
02:16Light Acquisition
8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K