Mapping Sugarcane in Central India with Smartphone Crowdsourcing

Lee, Ju Young; Wang, Sherrie; Figueroa, Anjuli Jain; Strey, Rob; Lobell, David B.; Naylor, Rosamond L.; Gorelick, Steven M.

doi:10.3390/rs14030703

Cited by 12 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For validation, we used a random sample over the four northeast regions (Liaoning, Nei Mongol, Jilin, and Heilongjiang), which span a much larger area than used in the training sample from Jilin. India Lee et al [35] produced a map of sugarcane area in the Upper Bhima Basin, a major sugarcane producing region in Maharashtra, India. Their 10 m resolution map is based on crowdsourced Plantix data and a neural network applied to S2 data.…”

Section: Chinamentioning

confidence: 99%

Annual Field-Scale Maps of Tall and Short Crops at the Global Scale Using GEDI and Sentinel-2

Di Tommaso,

Wang,

Vajipey

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain for many regions and years. NASA’s Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs. tall crops on a global scale at 10 m resolution for 2019–2021. Specifically, we show that (i) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (ii) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (iii) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. A systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction is sufficient to map individual crop types (e.g., maize vs. soy, sugarcane vs. rice). The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data.

show abstract

Section: Chinamentioning

confidence: 99%

Annual Field-Scale Maps of Tall and Short Crops at the Global Scale Using GEDI and Sentinel-2

Di Tommaso,

Wang,

Vajipey

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Meanwhile, regions with smallholder farms, which provide a living for two-thirds of the world's rural population of 3 billion (Lowder, Skoet, and Raney 2016) and produce 80% of the world's food (Economic et al 2014), continue to lack such maps. The majority of smallholder farms are located in middle-and low-income countries, where expensive ground data on crop types remains scarce (Tseng et al 2021;Wang et al 2020;Lee et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types

Laguarta Soler,

Friedel,

Wang

2024

AAAI

View full text Add to dashboard Cite

Accurate crop type maps are an essential source of information for monitoring yield progress at scale, projecting global crop production, and planning effective policies. To date, however, crop type maps remain challenging to create in low- and middle-income countries due to a lack of ground truth labels for training machine learning models. Field surveys are the gold standard in terms of accuracy but require an often-prohibitively large amount of time, money, and statistical capacity. In recent years, street-level imagery, such as Google Street View, KartaView, and Mapillary, has become available around the world. Such imagery contains rich information about crop types grown at particular locations and times. In this work, we develop an automated system to generate crop type ground references using deep learning and Google Street View imagery. The method efficiently curates a set of street-view images containing crop fields, trains a model to predict crop types using either weakly-labeled images from disparate out-of-domain sources or zero-shot labeled street view images with GPT-4V, and combines the predicted labels with remote sensing time series to create a wall-to-wall crop type map. We show that, in Thailand, the resulting country-wide map of rice, cassava, maize, and sugarcane achieves an accuracy of 93%. We publicly release the first-ever crop type map for all of Thailand 2022 at 10m-resolution with no gaps. To our knowledge, this is the first time a 10m-resolution, multi-crop map has been created for any smallholder country. As the availability of roadside imagery expands, our pipeline provides a way to map crop types at scale around the globe, especially in underserved smallholder regions.

show abstract

“…To minimize the yield loss and provide farmers with video training, the research uses CNN and numerous layers of Artificial Neural Network (ANN) with which they have created their custom deep learning algorithm. [5] have classified sugarcane images collected between 2019-2020; the researchers employed machine learning and an unsupervised approach by classifying all other crops and concentrating on classifying sugarcane as a different crop [6]. Moreover, [7] has proposed an Image-Based Disease Detection System Using Deep Learning for Tomato Leaves and Paddy Crop Disease Prediction Systems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Different Crop Leaf Disease Detection Using Convolutional Neural Network

Pawar¹,

Singh²,

Jadhav³

et al. 2023

Advances in Computer Science Research

View full text Add to dashboard Cite

Crop diseases are a considerable danger to the crop's health, affecting the yield. Timely detection is challenging due to a lack of infrastructure in many regions of the world. Since they result in the death of plants, the loss of their product, and the global food problem, plant diseases must be investigated. Crop disease detection has been made possible by recent advancements in computer vision, deep learning, and the growing worldwide adoption of smartphones. Convolutional Neural Networks have significantly improved classifying images in the past several years. The performance of deep learning-based techniques for plant disease recognition under actual circumstances is thoroughly examined in this research. The objective was to offer some principles for conducting a more thorough and realistic examination of deep learning-based approaches for disease recognition. Sequential Architecture was used to classify 38 diseases of 14 crops on a crop leaves image dataset containing 70,295 training and 17,572 testing images. A simple convolutional neural network has been proposed that detects crop diseases seamlessly. The maximum accuracy obtained was 95% on the 14 th epoch. This was accomplished by following the Sequential Model. It is a cuttingedge network that can help new researchers who desire to conduct their studies in deep learning applications with an emphasis on agriculture.

show abstract

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

Cited by 12 publications

References 42 publications

Annual Field-Scale Maps of Tall and Short Crops at the Global Scale Using GEDI and Sentinel-2

Annual Field-Scale Maps of Tall and Short Crops at the Global Scale Using GEDI and Sentinel-2

Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types

Different Crop Leaf Disease Detection Using Convolutional Neural Network

Contact Info

Product

Resources

About