Xanthomonas Wilt of Banana (BXW) is a complex problem in the African Great Lakes Region that is affecting the livelihoods of millions of smallholder farmers. Since the first disease reports from Uganda and the Democratic Republic of Congo in 2001, BXW has been studied widely. The majority of these studies focus on the technological or biophysical dimensions, while aspects and influence of socio-cultural, economic and institutional dimensions only recently started to gain attention.

Given its superior importance of digital agricultural solutions to overcome challenges in agricultural activities, many of the solutions are in face of challenges to scale in Sub-Saharan Africa (SSA).

Digitization in agriculture is rapidly advancing further on. New technologies and solutions were developed and get invented which ease farmers’ daily life, help them and their partners to gain knowledge about farming processes and environmental interrelations. This knowledge leads to better decisions and contributes to increased farm productivity, resource efficiency, and environmental health. Along with numerous advantages, some negative aspects and dependencies risk seamless workflow of agricultural production.

CONTEXT: Adoption and diffusion of digital farming technologies are expected to help transform current agricultural systems towards sustainability. To enable and steer transformation we need to understand the mechanisms of adoption and diffusion holistically. Our current understanding is mainly informed by empirical farm-level adoption studies and by agent-based models simulating systemic diffusion mechanisms. These two approaches are weakly integrated.

Digitalisation is an integral part of modern agriculture. Several digital technologies are available for different animal species and form the basis for precision livestock farming. However, there is a lack of clarity as to which digital technologies are currently used in agricultural practice. Thus, this work aims to present for the first time the status quo in Swiss livestock farming as an example of a highly developed, small-scale and diverse structured agriculture.

This article extends social science research on big data and data platforms through a focus on agriculture, which has received relatively less attention than other sectors like health. In this paper, I use a responsible innovation framework to move attention to the social and ethical dimensions of big data “upstream,” to decision-making in the very selection of agricultural data and the building of its infrastructures.

Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems.

For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.

3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach.

This paper proposes a bionic electric spraying rod to perform the crop watering and spraying in the farm. The design concept of multiple vertebrae structures of snake is used to realize a reproducible snake bone arm and muscles of snake, which can be regarded as multiple sets of thin wires and be pulled and released through driver module. It results in different attitudes of the snake bone arm. A water pipe is installed in the snake arm connected to the spray nozzle for spraying. The mobile application interface (APP) is designed to provide the user to control the arm remotely.