Artificial intelligence and satellite‐based remote sensing can be used to predict soybean ( Glycine max ) yield

Weed encroachment negatively affects pasture productivity by reducing herbage allowance, stocking rates, and livestock performance. Amaranthus spinosus L. is a weed species widely found in pastures worldwide and is considered challenging for ranchers due to its great potential for invasion, making it difficult to control. The high costs of chemical application and the global concern about environmental impacts restrict indiscriminate herbicide spraying in pastures. Site‐specific weed management (SSWM) is a weed management strategy based on weed spot‐spraying that has the potential to overcome these issues. Images from unmanned aerial vehicles (UAVs) can provide valuable information for weed mapping to drive the herbicide application in pastures. Deep learning techniques have been highlighted in image classification tasks. We developed a deep convolutional neural network (CNN)‐based image segmentation model based on the U‐Net architecture to detect and map Amaranthus spinosus in bermudagrass pastures using red–green–blue images acquired through UAV flying in moderate‐high altitude. The images were acquired from twelve paddocks under three treatments (weed‐free, weed‐strips, or weed‐infested) during the summer (2021‐2022). The CNN model was able to detect around 80% of the A. spinosus with an average prediction accuracy of 94%. Our weed mapping showed the potential of using the U‐Net model to generate a herbicide application map to be inserted into the sprayer system, reducing up to 76% of the amount of herbicide applied. Further studies are encouraged to increase the robustness of the model across species and development stages and develop sprayer systems to implement the spot‐spraying in field conditions.

Section: The U-net Architecturementioning

confidence: 99%

Detection and mapping of Amaranthus spinosus L. in bermudagrass pastures using drone imagery and deep learning for a site‐specific weed management

Bretas,

Dubeux,

Zhao

et al. 2024

Will artificial intelligence and machine learning change agriculture: A special issue

Clay,

Brugler,

Joshi

2024

Self Cite

In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning.

Soybean prediction using computationally efficient Bayesian spatial regression models and satellite imagery

Fischer,

Rekabdarkolaee,

Joshi

et al. 2024

Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP; R2‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR; R2‐testing = 0.02), Bayesian spatial regression (SRM; R2‐testing = 0.087), and Bayesian skewed spatial regression (sSRM; R2‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lower R2 values and higher execution times than the random forest model.