News
Agriculture is considered one of the main factors for economic growth and a source of employment in developing countries like Brazil. The agricultural sector contributes 27.4% of the national GDP, showing its importance and commercial strength. Now, with artificial intelligence in agriculture, the possibilities for growth are even greater. Agricultural activities are broadly categorized into three main areas: pre-harvest, harvest, and post-harvest. Within these categories, the use of machine learning can improve productivity gains in agriculture. Machine learning is the current technology that is benefiting farmers in minimizing losses in agriculture, providing recommendations and rich insights about their respective harvests and crops. The technology can help farmers make better decisions and alleviate farming-related problems. Technologies like Blockchain, Internet of Things, Machine Learning, deep learning with Artificial Intelligence, cloud computing, and edge computing can be used to gather and process information. Computer vision, machine learning, and IoT applications will help increase production, improve quality, and ultimately increase the profitability of farmers and associated domains. Data accuracy in the agricultural field is very important for improving overall crop yield. Let's find out how agriculture can benefit from Machine Learning technology below, with some possibilities. READ ALSO: • What is remote sensing
• Agricultural technology
Speak with one of our specialists and discover how Pix Force can transform your business
Artificial intelligence in agriculture: Crop and plant disease detection
By 2050, human crop yields will need to increase by about 70% to meet the needs of the expected population size. Crop diseases currently reduce the yield of the six most important food crops by 42%, and some farms are completely wiped out annually. Therefore, it becomes extremely important to find methods leveraging technology for the accurate detection of crop diseases. This is where Machine Learning techniques can help. Deep learning algorithms can be trained on images of crops and plants with good accuracy for detecting diseases that are affecting crops and harming productivity. One of the most widely used technologies today for obtaining these images is Unmanned Aerial Vehicles (UAVs) combined with large-scale back-end systems involving machine learning models to detect crop diseases. In order to address the challenge associated with data collection, modeling techniques such as Generative Adversarial Networks (GANs) can be used to generate synthetic data using images of the diseases affecting the crops. Another challenge for training models with high accuracy is class imbalance in the collected data. This is where DC-GAN (Deep Convolutional GAN) plays a key role in alleviating the class imbalance problem by generating synthetic images. A deep convolutional neural network (CNN) model could then be trained to classify and detect crop/plant diseases. The CNN model can be trained to identify diseases that have made a physical presence on the leaf and/or stem of the crop and detect which specific pests are doing this. It is important to remember that generative adversarial networks are pairs of neural networks that are divided into two functions: 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 each other to improve results.
Crop yield prediction with artificial intelligence?
It is possible to accurately predict crop yield through artificial intelligence. This will help farmers know when they should start harvesting so they can maximize their profits by selling their inputs at an appropriate price. Crop yield prediction is about predicting the expected yield of agricultural crops in a given period, and this crop yield prediction is extremely challenging due to its dependence on multiple factors, such as crop genotype, environmental factors, management practices, and their interactions. Machine learning models are built to predict crop yield, taking into account different factors that affect it, such as climate data (temperature, precipitation), soil moisture sensors, astronomy images, etc., predicting precise yield values for an agricultural field before harvesting. These techniques can be used by farmers daily with high accuracy, enabling them to make decisions about when to harvest crops, how much pesticide needs to be applied, and which fertilizers are to be used. Machine Learning models can be used to predict agricultural production on a large scale with an accurate yield estimate. This will help farmers decide on cropping patterns and crop management, leading to better yields during harvest season. Algorithms such as multilinear regression, Lasso regression, LightGBM, random forest, XGBoost, and deep neural networks (CNN, LSTM) have been used for crop yield predictions in agriculture.
Identification of water stress in crops
Water stress in a plant can occur due to the limited availability of water to the roots/soil or due to increased transpiration. These factors adversely affect the plant's physiology and photosynthetic capacity to the extent that they have been shown to have inhibitory effects on both growth and yield. Early identification of the plant's water stress status allows appropriate corrective measures to be applied to achieve 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 the stress. This is where Machine Learning techniques come into play: machine learning algorithms can be used in estimating leaf water content, which is then used to estimate water stress in plants. Leaf water content (LWC) is a measurement that can be used to estimate water content and identify stressed plants. LWC during the early stages of crop growth is an important indicator of plant productivity and yield. Different techniques can be used for data collection. These include the use of sensors or UAVs. The use of sensors can, however, be very expensive. Classification and regression methods can be used to predict the LWC value. And classification models can be used to classify water stress based on LWC and other parameters.
Crop mapping with artificial intelligence
Field-level crop type mapping is fundamental for a variety of applications in agricultural monitoring. Mapping the crop type at field resolution is a prerequisite for mapping farm management and yield outcomes on a large spatial scale. This task is even more urgent at a time when populations in food-insecure regions continue to increase and climate change is expected to negatively affect global agriculture. Traditionally, crop type information is obtained from field surveys and censuses, but such surveys are expensive and time-consuming to conduct. This is where machine learning techniques are applied to satellite data for crop type maps. Classification algorithms such as LDA, random forest can be used for crop classification and mapping.
Crop Selection Forecast
Machine Learning can be used to help farmers select crops efficiently and maximize crop yield with minimal cost. Machine learning models can be trained to predict the most appropriate crop selection and yield for different regions. It will be necessary to select different types of crops, identify characteristics, and then train the model to classify crop selection for different regions. Algorithms such as SVM, random forest, logistic regression, deep neural networks, etc., can be used to train such models. The features used in such models can be related to weather parameters (rainfall, temperature, etc.), fertilizers used, terrain type, soil-related information, etc.
Irrigation detection
Irrigation detection is fundamental to understanding water use and promoting better water management. This data will potentially allow the study of the impact of climate change on agricultural water sources, monitor water use, help detect water theft and illegal farming, and inform policy decisions and regulations related to water compliance and management. Machine Learning, in this case, 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 available curated and labeled data centered on irrigation systems. This is where pre-trained models can help. These will be treated as classification models, and the target label is a binary variable indicating whether the land in the image is permanently irrigated or not. CNN network models can be trained to classify the land as irrigated or not.
Groundwater Level Forecasting
Groundwater is the largest reservoir of freshwater resources, serving as the main supply for most human consumption through agricultural, industrial, and domestic water delivery. Deep neural networks can be trained to predict groundwater levels. Deep learning methods are known to produce accurate results even with the limited information available in this case, which mainly consists of satellite data and hydrometeorological parameters. Machine learning applications are more prevalent in agriculture than you might think. The agricultural sector has a lot of data, but without machine learning models for optimal crop selection and yield forecasting, it is difficult to harness its full potential. And this is where Pix Force can help agribusiness by offering solutions through our success stories and the support of our specialists. We can provide access to our team's expertise in Artificial Intelligence and Machine Learning so that the producer doesn't have to worry about what lies ahead on the horizon when it comes to agricultural innovations. Get in touch!
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
