The specialized crops could save farmers millions. A swarm of desert locusts fly after an aircraft sprayed pesticide in Meru, Kenya in 2021. Breakthroughs, discoveries, and DIY tips sent six days a week. Swarms of locusts devouring a farmer's livelihood might sound apocalyptic, but major locust infestations are a regular problem in agricultural communities around the world. These locust swarms--dense, droning packs of certain grasshopper species--can cover hundreds of square miles, and the insects consume vast amounts of vegetation and threaten global agriculture.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: This paper attempts to link sparse optimization methodology to the anatomical structure of locust's early olfactory system. The work is motivated by the observation that odorant molecules are sparsely represented by the population of Kenyon cells. The authors first mathematically formulate the olfactory system as a MAP decoder, and give the standard solution to the problem without considering biological constraints. Next, to make the solution more biologically plausible, the authors reformulate the olfactory system model as a decoder of a compressive sensing problem, and provide two standard solutions to the dual problem. Then, the authors argue that each of the components in the solution can be mapped/interpreted to/as a unit of the biological structure in the olfactory system. However, these maps are described without a strong justification and there are conceptual problems in linking the math with the biology.

Many species show avoidance reactions in response to looming object approaches.

Compared to human vision, insect visual systems excel at rapid and precise collision detection, despite relying on only tens of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs), which respond specifically to approaching objects. Research upon LGMD neurons began in the early 1970s. Initially, due to their large size, these neurons were identified as motion detectors, but their role as looming detectors was recognized over time. Since then, progress in neuroscience, computational modeling of LGMD's visual neural circuits, and LGMD-based robotics has advanced in tandem, each field supporting and driving the others. Today, with a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots including ground and aerial robots. This review highlights recent developments in LGMD research from the perspectives of neuroscience, computational modeling, and robotics. It emphasizes a biologically plausible research paradigm, where insights from neuroscience inform real-world applications, which would in turn validate and advance neuroscience. With strong support from extensive research and growing application demand, this paradigm has reached a mature stage and demonstrates versatility across different areas of neuroscience research, thereby enhancing our understanding of the interconnections between neuroscience, computational modeling, and robotics. Furthermore, other motion-sensitive neurons have also shown promising potential for adopting this research paradigm.

Desert locust swarms present a major threat to agriculture and food security. Addressing this challenge, our study develops an operationally-ready model for predicting locust breeding grounds, which has the potential to enhance early warning systems and targeted control measures. We curated a dataset from the United Nations Food and Agriculture Organization's (UN-FAO) locust observation records and analyzed it using two types of spatio-temporal input features: remotely-sensed environmental and climate data as well as multi-spectral earth observation images. Our approach employed custom deep learning models (three-dimensional and LSTM-based recurrent convolutional networks), along with the geospatial foundational model Prithvi recently released by Jakubik et al., 2023. These models notably outperformed existing baselines, with the Prithvi-based model, fine-tuned on multi-spectral images from NASA's Harmonized Landsat and Sentinel-2 (HLS) dataset, achieving the highest accuracy, F1 and ROC-AUC scores (83.03%, 81.53% and 87.69%, respectively). A significant finding from our research is that multi-spectral earth observation images alone are sufficient for effective locust breeding ground prediction without the need to explicitly incorporate climatic or environmental features.

When the gravity acting on them is increased, locusts adapt. Locusts placed in a centrifuge to mimic the conditions of hypergravity grew tougher legs than those living normally – but not all of them survived the process. Many biological materials, such as bone and wood, can adapt and become stronger under physical strain, but it isn't clear whether animals with shell-like exoskeletons can adapt in the same way as those with internal skeletons. Karen Stamm and Jan-Henning Dirks at the City University of Applied Sciences in Bremen, Germany, studied this by placing locusts inside a specially designed centrifuge to stress-test their exoskeletons using simulated hypergravity. The locusts were assigned to one of four gravity conditions: 1g – which is typical gravity at sea level and didn't involve a centrifuge – and 3g, 5g or 8g conditions, all of which did involve centrifuging the insects.

Productionizing Machine Learning models is a complicated practice. There's a lot of iteration around different model parameters, hardware configurations, traffic patterns that you will have to test to try to finalize a production grade deployment. Load testing is an essential software engineering practice, but also crucial to apply in the MLOps space to see how performant your model is in a real-world setting. How can we load test? A simple yet highly effective framework is the Python package: Locust. Locust can be used in both a vanilla and distributed mode to simulate up to thousands of Transactions Per Second (TPS).

You've spent a lot of time on EDA, carefully crafted your features, tuned your model for days and finally have something that performs well on the test set. Now, my friend, we need to deploy the model. After all, any model that stays in the notebook has a value of zero, regardless of how good it is. It might feel overwhelming to learn this part of the data science workflow, especially if you don't have a lot of software engineering experience. Fear not, this post's main purpose is to get you started by introducing one of the most popular frameworks for deployment in Python -- Flask.

The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your model. System tests are carried out to test the robustness of the design of a system for given inputs and expected outputs (for example, an MLOps pipeline, inference). Acceptance tests (to fulfill user requirements) can be performed as part of system tests. A/B testing is performed by sending production traffic to alternate systems that will be evaluated.

MLOps knowledge has become one of the major skills that one machine learning engineer can have. However, putting a machine learning model into production successfully is not an easy task. It requires a wide range of software development and DevOps abilities in addition to data science understanding. In a nutshell, in order to increase your value as a machine learning engineer, you must not only understand how to apply various Machine Learning and Deep Learning models to a specific problem, but also how to test, verify, and deploy them. Having someone who can put Machine Learning models into production has become a major benefit for any business. One of the final problems, when it comes to putting Machine Learning models into production, is verifying that API that is serving this model is having good performance.