A study by researchers from Japan has shown that artificial intelligence (AI) can be used to estimate rice yields. The study, which was published in the journal Plant Phenomics, used ground-based digital images taken at the harvesting stage of the crop, combined with convolutional neural networks (CNNs), to estimate rice yield.
Convolutional neural networks (CNNs) are a type of feed-forward neural network that learns feature engineering by itself via filters optimization. They are designed to emulate the behavior of a visual cortex and mitigate the challenges posed by the multilayer perception (MLP) architecture by exploiting the strong spatially local correlation present in natural images.
Applications
CNNs have applications in:
- image and video recognition
- recommender systems
- image classification
- image segmentation
- medical image analysis
- natural language processing
- brain–computer interfaces
- financial time series
The study was conducted in 20 locations in seven countries. The researchers gathered rice canopy images and rough grain yield data from each location. They then used this data to train a CNN model to estimate rice yield.
Capabilities of the Developed CNN Model
- The model was able to explain around 68%-69% of yield variation in the validation and test datasets. The researchers say that this is a promising result, as it suggests that AI can be used to accurately estimate rice yields.
- The model was also able to identify the importance of panicles — loose-branching clusters of flowers — in yield estimation. The model could predict yield accurately during the ripening stage, recognizing mature panicles, and also detect cultivar and water management differences in yield in the prediction dataset.
- The study’s findings suggest that AI has the potential to be used to monitor rice productivity at regional scales.
However, the researchers say that further research is needed to adapt the model to low-yielding and rainy environments.
The AI-based method has also been made available to farmers and researchers through a simple smartphone application, thus greatly improving accessibility of the technology and its real-life applications. The name of this application is HOJO, which is used for recording the growth of crops. The app’s ability to link and manage location, date, and time information allows it to support the work of users who want to take growth profiles in expansive fields and field workers who want to compare fields in various places. It is already available on iOS and Android.
The researchers hope that their work will lead to better management of rice fields and assist accelerated breeding programs, contributing positively to global food production and sustainability initiatives.
