AIMC Topic: Agriculture

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Understanding the spread of agriculture in the Western Mediterranean (6th-3rd millennia BC) with Machine Learning tools.

Nature communications
The first Neolithic farmers arrived in the Western Mediterranean area from the East. They established settlements in coastal areas and over time migrated to new environments, adapting to changing ecological and climatic conditions. While farming prac...

Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning.

PloS one
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, hav...

Utilizing convolutional neural network (CNN) for orchard irrigation decision-making.

Environmental monitoring and assessment
Efficient agricultural management often relies on farmers' experiential knowledge and demands considerable labor, particularly in regions with challenging terrains. To reduce these burdens, the adoption of smart technologies has garnered increasing a...

Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain.

Environmental monitoring and assessment
Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. ...

An efficient smart phone application for wheat crop diseases detection using advanced machine learning.

PloS one
Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges fro...

Gripping Success Metric for Robotic Fruit Harvesting.

Sensors (Basel, Switzerland)
Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the mod...

Extraction of agricultural plastic greenhouses based on a U-Net convolutional neural network coupled with edge expansion and loss function improvement.

Journal of the Air & Waste Management Association (1995)
Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more ...

Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning.

PloS one
Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduce...

Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images.

Sensors (Basel, Switzerland)
Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and f...

Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India.

Environmental monitoring and assessment
Paddy crop mapping is essential for agricultural monitoring, ensuring food security, and enhancing resource allocation. This study observes the Cauvery Delta Zone (CDZ), recognized as the rice bowl of Tamil Nadu and a crucial area for paddy farming i...