Agriculture in West Africa is constrained by several yield-limiting factors, such as poor soil fertility, erratic rainfall distributions and low input systems. Projected changes in climate...
Weed detection has become an integral part of precision farming that leverages the IoT framework. Weeds have become responsible for 45% of the agriculture industry's crop losses due mainly to the competition with crops. An efficient weed detection method can reduce this percentage. This paper proposes a vision-based weed detection system using deep learning models that effectively detect weed within a soybean plantation. Five deep learning models are used, including MobileNetV2, ResNet50, and three custom Convolutional Neural Network (CNN) Models. The MobileNetV2 and ResNet50 were deployed on a Raspberry PI controller for comparison purposes. Based on a dataset with 400 images and 1536 total segments, the custom 5-layer CNN architecture shows high detection accuracy of 97.7% and the lowest latency & memory usage with 1.78 GB and 22.245 ms respectively. Utilizing the proposed custom deep learning CNN model with high accuracy can positively impact efficiency, time, and overall production within the soybean industry.
|Year of publication|
Journal of Agriculture and Food Research
16 May 2022
|Knowledge service | Metadata||Global Food and Nutrition Security |Research and Innovation|
|Digital Europa Thesaurus (DET)||crop productionartificial intelligenceartificial neural networkModellingAgricultureInternet of Things|
Agriculture in Central Asia is vulnerable to climate change due to rising aridity, declining availability of water resources for irrigation, and low adaptive capacity. We use climate...
In many Sub-Saharan countries, farmers cannot meet the growing urban demand for higher quality products, leading to increasing dependency on imports. While the literature has...