Collaborating Authors

Deep learning in agriculture: A survey Machine Learning

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

How AI can help identify weeds and increase field productivity and profitability – AI.Business


In a world reached a population of six billion humans increasingly demand it for food, feed with a water shortage and the decline of agricultural land and the deterioration of the climate needs 1.5 billion hectares of agricultural land and in case of failure to combat pests needs about 4 billion hectares. Weeds represent 34% of the whole pests while insects, diseases and the deterioration of agricultural land present the remaining percentage. Weeds identification has been one of the most interesting classification problems for Artificial intelligence and image processing. The most common case is to identify weeds within the field as they reduce the productivity and harm the existing crops. Success in this area results in an increased productivity, profitability and at the same time decreases the cost of operation.

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning Machine Learning

Robotic weed control has seen increased research in the past decade with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for arable croplands, ignoring the significant weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust detection of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the highly complex Australian rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust detection methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper also presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification performance of 87.9% and 90.5%, respectively. This strong result bodes well for future field implementation of robotic weed control methods in the Australian rangelands.

Real World Deep Learning: Neural Networks for Smart Crops


To produce high-quality food and feed a growing world population with the given amount of arable land in a sustainable manner, we must develop new methods of sustainable farming that increase yield while minimizing chemical inputs such as fertilizers, herbicides, and pesticides. I and my colleagues are working on a robotics-centered approaches to address this grand challenge. My name is Andres Milioto, and I am a research assistant and Ph.D. student in robotics at the Photogrammetry and Robotics Lab ( Together with Philipp Lottes, Nived Chebrolu, and our supervisor Prof. Dr. Cyrill Stachniss we are developing an adaptable ground and aerial robots for smart farming in the context of the EC-funded project "Flourish" (, where we collaborate with several other Universities and industry partners across Europe. The Flourish consortium is committed to develop new robotic methods for sustainable farming that aim at minimizing chemical inputs such as fertilizers, herbicides, and pesticides in order to reduce the side-effects on our environment.

UAV-based crop and weed classification for future farming


Crops are key for a sustainable food production and we face several challenges in crop production. First, we need to feed a growing world population. Second, our society demands high-quality foods. Third, we have to reduce the amount agrochemicals that we apply to our fields as it directly affects our ecosystem. Precision farming techniques offer a great potential to address these challenges, but we have to acquire and provide the relevant information about the field status to the farmers such that specific actions can be taken.