Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning

Abstract

Land suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013–2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. Through k-fold cross-validation, we show that compared to the single crop models, our multi-crop model could produce up to a 2.82 fold reduction in mean absolute error for any particular crop. We found that barley, oats, and mixed grains were more tolerant to soil-climate-landscape variations and could be grown in many regions of Canada, while non-grain crops were more sensitive to environmental factors. Predicted crop suitability was associated with a region’s growing season length, which supports climate change projections that regions of northern Canada will become more suitable for agricultural use. The proposed multi-crop model could facilitate assessment of the suitability of northern lands for crop cultivation and be incorporated into cost-benefit analyses.

Assessing the Interpretability–Performance Trade-Off of Artificial Neural Networks Using Sentinel Fish Health Data

“…However, many of these studies are either not interested in a mechanistic explanation of the system considered, or the system is already well defined. For instance, Bhullar et al (2023) [41] investigated the use of deep-NNs for assessing how land suitability will change due to the effects of climate change. In this scenario, the system being analyzed, and the relationship between the environment and land suitability, is well understood, and so model accuracy is prioritized over interpretability [41].…”

Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries

“…The performance analysis of the mean square error and standard deviation showed high accuracy compared to other models in the field. Bhullar et al (2023) [30] presented a data-driven multi-layer perceptron that predicted the land suitability of several crops in Canada. The crop yield dataset included several types of crops, such as barley, peas, spring wheat, canola, oats, and soy, collected between 2013 and 2020.…”

“…A few scholars have investigated the agricultural situation and the ways that it can be used to enhance the productivity of conventional freshwater agriculture in the Gulf region. Researchers have studied the limited water sources (i.e., produced water, groundwater, and seawater) and how farmers can benefit from them when planting different crops [15][16][17][18][30][31][32][34][35][36]. In addition, the majority of the models proved that using a neural network model is more efficient in crop yield prediction.…”

Mental Pressure Recognition Method Based on CNN Model and EEG Signal under Cross Session

“…However, when the signal propagates from the source to the scalp, the expression of deep neuron activity related to MP changes may be disturbed. In order to make the best use of the EEG characteristics of deep neurons, a feasible method aims to establish a hierarchical deep structure to reconstruct the source of hidden features [19,20]. In this case, the shallow layer of the deep learning model can be regarded to be a filter to capture the best feature combination of the external scalp EEG information.…”