Today agriculture is in a state of flux. Farmers are faced with the challenges of producing more food in face of a changing climate and population growth, while also adapting to evolving technologies that have changed agriculture forever. Machine learning has been applied to agriculture for many different use cases, from irrigation scheduling to pest management. In this post, we will explore agriculture use cases for machine learning & deep learning that can help farmers meet these challenges head-on. Different machine learning applications can be built around these agricultural use cases. It will be helpful for data scientists to get a high level idea around use cases and related machine learning techniques.
What are different agriculture use cases for machine learning?
Before getting into details in relation to different machine learning techniques for agriculture, lets understand the concept of precision agriculture in which machine learning plays a key role.
Precision agriculture is an agriculture domain in which the machine learning solutions are used in the manner in which the farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. The key driver for this capability is fast, robust and accurate machine learning modelling. Precision agriculture technologies can be used by the farmers to increase their farm yield, reduce costs and protect the environment.
Here are few important machine learning applications / use cases for agricultural domain:
- Plants and crops disease detection: By 2050, human agricultural crop yield will need to increase by an estimated 70 percent to sustain the expected population size. By a particular statistics, crop diseases currently reduce the yield of the six most important food crops by 42 percent, and some farms are wiped out entirely on an annual basis. Thus, it becomes of utmost importance to find methods by leveraging technology for accurate crop disease detection. This is where machine learning techniques can help. Deep learning algorithms can be trained on crops and plants images with good accuracy for crop disease detection. The challenge is to get the plant/crop images and then labelled images. One of the prevalent techniques used currently is Unmanned Aerial Vehicles (UAVs) paired with large-scale backend systems involving machine learning models for detecting crop diseases. In order to address the challenge associated with data collection, modeling technique such as generative adversarial network (GAN) can be used to generate synthetic data using the crop disease images. One of the other challenge for training models having high accuracy is class imbalance in the collected data. This is where DC-GAN (Deep convolutional GAN) plays a great role to alleviate the class imbalance issue by generating synthetic images. A deep convolutional neural network (CNN) model (such as EfficientNet) can then be trained to classify and detect crops / plants diseases. The CNN model can be trained to identify ailments that made their physical presence on the leaf and/or stem of the crop and detect diseases. Recall that generative adversarial networks are pairs of neural networks that are divided into two roles: generator and discriminator. The generator learns to develop synthetic images of some class, while the discriminator learns to discern between real and synthetic images. The models train off of each other to improve results.
- Crop yield prediction: Predicting the crop yield accurately will help farmers know when they should start harvesting so that they can maximize their profits by selling it at an appropriate price. Crop yield prediction is about forecasting the expected yield of agriculture crops in a given period. Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. The machine learning models are built to predict crop yields by taking into consideration different factors that affect it such as weather data (temperature, rainfall), soil moisture sensors, astronomy images etc., thus predicting accurate yield values for an agriculture field before harvest time. These techniques can be used by farmers on daily basis with high accuracy which enable them to make decisions on when to harvest crops, how much pesticide needs to be applied and what fertilizers need to be used. Deep learning models can be used to predict agriculture production in large scale with an accurate estimation of the yield. This will help farmers make important decisions related to cropping patterns and crop management leading to better yields during harvest season. Algorithms such as multi-linear regression, Lasso regression, LightGBM, random forest, XGBoost and deep neural networks (CNN, LSTM etc) have been used for crop yield predictions.
- Crop row detection: Crop row detection is a key element in developing vision based navigation robots in agricultural robotics. Recent work on crop row detection has used deep learning based methods thereby overcoming the major challenges in implementing a real world vision based navigation system. Some of the key aspects in crop row detection are weed density, growth stages, shadows and discontinuities etc. CNN architectures can be used for crop row detection based on weed density, growth stages, shadows etc.
- Identify water stress in plant: Plant water stress may occur due to the limited availability of water to the roots/soil or due to increased transpiration. These factors adversely affect plant physiology and photosynthetic ability to the extent that it has been shown to have inhibitory effects in both growth and yield. Early identification of plant water stress status enables suitable corrective measures to be applied to obtain the expected crop yield. It is necessary to identify potential plant water stress during the early stages of growth to introduce corrective irrigation and alleviate stress. This is where machine learning techniques come into picture. Machine learning techniques can be used to estimate leaf water content (LWC) which is then further used to estimate water stress in the plants. Leaf water content (LWC) is a measure that can be used to estimate water content and identify stressed plants. LWC during the early crop growth stages is an important indicator of plant productivity and yield. Different techniques can be used for data collection. They include usage of sensors or UAVs. Usage of sensors can, however, prove to be very expensive. Ensemble and regressor methods can be used to predict the LWC value. And, classification models can be used to classify the water stress based on LWC and other parameters.
- Crop mapping: Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring. Crop type mapping at the field resolution is a prerequisite to mapping farm management and yield outcomes at large spatial scale. This task is all the more urgent in a time when populations in food-insecure regions continue to increase and climate change is predicted to adversely affect global agriculture. Traditionally, crop type information has been obtained from field surveys and censuses, but such surveys are expensive and time consuming to conduct. This is where machine learning techniques are applied on the satellite data for crop type maps. Classification algorithms such as LDA, random forest can be used for crop classification & mapping.
- Crop selection prediction: Machine learning techniques can be used to help the farmers select the crop efficiently and maximize crop yield with minimal cost. The machine learning models can be trained to predict most appropriate crop selection and yield for different regions. One will be required to select different types of crops, identify features and then train model to classify the crop selection for different regions. Algorithms such as SVM, random forest, logistic regression, deep neural networks etc, can be used to train such models. Features used in such models can be related to weather parameters (rainfall, temperature etc.), fertilizers used, land type, soil-related information etc.
- Irrigation detection: Detecting irrigation is critical to understand water usage and promote better water management. Such data will potentially enable the study of climate change impact on agricultural water sources, monitor water usage, help detect water theft and illegal agriculture and inform policy decisions and regulations related to water compliance and management. Machine learning techniques can be used for irrigation detection. However, this is a complex problem to solve with the help of ML techniques due to the lack of curated and labelled data available that are centered around irrigation systems. This is where pre-trained models can help. This will be classification models and the target label is a binary variable indicating whether the land in the image is permanently irrigated or not. CNN models can be trained to classify the land as irrigated or otherwise.
- Ground water level prediction: Groundwater is the largest storage of freshwater resources, which serves as the major inventory for most of the human consumption through agriculture, industrial, and domestic water supply. Deep neural networks can be trained to forecast ground-water levels. Deep learning methods are known to produce accurate results even with the limited information available in this case, which is mostly satellite data and hydro-meteorological parameters.
Machine learning applications are more prevalent in agriculture than you might think. The agriculture industry has a lot of data, but they may not know how to leverage it with machine learning models for better crop selection and yield prediction. This is where we can help you. We can provide access to our team’s expertise in AI & machine learning so you don’t have to worry about what’s next on the horizon when it comes to farming innovations!